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Record W4361002374 · doi:10.1111/jgs.18335

The impact of educational whiteboard videos on healthcare providers' self‐efficacy to deprescribe

2023· article· en· W4361002374 on OpenAlexafffundabout
Justin P. Turner, Camille Gagnon, Ninh B. Khuong, Emily G. McDonald, Cara Tannenbaum

Bibliographic record

VenueJournal of the American Geriatrics Society · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversité de MontréalMcGill University Health CentreInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health Research
KeywordsDeprescribingMedicineHealth careWhiteboardNursingMultimediaPolypharmacyComputer science

Abstract

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As least 34% of older adults are exposed to one or more potentially inappropriate medications (PIMs).1 Although deprescribing can be an effective intervention, not all healthcare providers feel equipped to deprescribe.2 In part, this could be due to a lack of self-efficacy—an individual's belief in their capacity to accomplish the task. Improvements in healthcare provider self-efficacy with deprescribing could increase engagement and translate to positive behavioral changes.3 Online learning modules can be used to scale up educational interventions and may improve self-efficacy for professional tasks. Whiteboard videos, a type of online learning module, consist of concise animated videos presenting information with real-time illustration and narration.4 While pragmatic and scalable, limited research has quantified their impact on healthcare provider self-efficacy, especially in geriatrics. In this study, we aimed to (1) create deprescribing whiteboard videos and (2) assess the impact on healthcare provider self-efficacy on deprescribing. Four whiteboard videos were created to increase healthcare providers' self-efficacy in deprescribing. The first video, entitled "Why Deprescribe," debunked commonly perceived barriers to deprescribing, such as lack of time, lack of evidence to support deprescribing, or a fear of causing symptoms to return. Subsequent videos focused on deprescribing three common classes of PIMs: sedatives, proton-pump inhibitors (PPIs), and opioids. Each medication-specific whiteboard video addressed key steps in the deprescribing process: for example, how to engage patients in a deprescribing conversation, how to select viable non-pharmacologic treatment alternatives, and how to write a tapering protocol. To maximize changes in healthcare provider engagement, scripting and illustrations were chosen to reflect relevant domains from the Behavior Change Techniques Taxonomy (v1).5 The videos each ranged between 3 and 4 min duration, as brevity was considered critical for engaging busy frontline providers. Whiteboard videos were posted online (www.DeprescribingNetwork.ca/whiteboard-videos) and promoted via social media accounts, by international deprescribing networks, and leaders in the field. Invitations to participate directed respondents to the website and included electronic informed consent to collect survey data and basic anonymous demographics (e.g., age, sex, and profession). Non-incentivized respondents were provided with survey questions pre- and post-video viewing and could select one or more videos, in any order. Unique respondents were counted by computer internet protocol (IP) address used to answer the surveys. For example, if one IP address was used to answer a survey on PPIs and sedatives, this was counted as two responses (one respondent). To assess self-efficacy, respondents were asked to rate their level of confidence for key deprescribing domains (0 = I cannot do at all, through to 10 = Highly confident I can do). The mean self-efficacy score was calculated for each question and compared pre- and post-video watching. Higher scores indicated improved self-efficacy. Scores were then dichotomized with ≥7 out of 10 indicating high self-efficacy. The proportion of respondents who reported high self-efficacy for each of the key deprescribing domains were compared pre- and post-viewing for each of the three medication classes. Mean scores pre- and post-video viewing were compared with paired t-tests and changes in proportions via McNemar's test. All analyses were conducted using SPSS (IBM SPSS, Chicago, IL; version 26). Research ethics board approval was obtained from the comité d'éthique de la recherche—vieillissement et neuroimagerie, Centre intégré universitaire de santé et de services sociaux du Centre-sud-de-l'île-de-Montréal (CER VN 18-19-12). Between January 2019 and October 2020, whiteboard videos were viewed on 1028 occasions from 212 unique IP addresses; pre- and post-surveys were completed on 368 (35.8%) occasions. We collected 122 responses for the "Why Deprescribe" video, 115 for sedatives, 88 for PPIs and 43 for the opioids. Of the 368 responses, 82.3% were from women (n = 303); 34.8% (n = 128) were from pharmacists, 27.7% (n = 102) from nurses, 19.3% (n = 71) from physicians and 18.2% (n = 67) from another profession (e.g., students, administrators, researchers); 169 (45.9%) of viewings were in French. The number of responses (n = 368) to surveys was more than the number of unique IP addresses (n = 212) as one respondent could view and respond to one or more videos/surveys, but was not obliged to view and respond to all four. After viewing the "Why Deprescribe" video, the proportion of respondents reporting high self-efficacy rose from 36.1% (44/122) to 63.9% (78/122) for deprescribing PIMs (p < 0.001) and from 65.6% (80/122) to 74.6% (91/122) for evidence-based prescribing (p = 0.02). Similarly, for each PIM-specific video, the proportion of respondents who identified as having high self-efficacy rose significantly for each of the three key deprescribing domains (Figure 1). Mean scores for each of the key deprescribing domains also improved across all domains (Table S1). In this study, we found that theory-informed, evidence-based whiteboard videos led to significant improvements in self-efficacy to deprescribe. Strengths of this study included behavior change theory and whiteboard educational theory to inform the intervention. The videos included medication classes commonly prescribed to older adults and addressed a pressing clinical problem. Frontline healthcare providers were likely interested in watching the videos given there were over 1000 viewings. From a feasibility perspective, the brevity of the videos and the gains in self-efficacy suggest the intervention met the needs of busy healthcare providers. In future, partnering with continuing education providers to promote these videos as accredited content may be a promising approach to increase their reach. Additionally, whiteboard videos may be an effective means of teaching other topics in geriatrics. A limitation is the number of viewers completed the post-video surveys. This rate of response is common among healthcare providers; however, it might have resulted in responder (selection) bias. Although we pursued multiple avenues, recruitment from social media sites of deprescribing networks may have selected healthcare providers with an interest in deprescribing. Still, rates of self-efficacy were low at baseline, and if anything, this type of selection bias would have underestimated the impact. Finally, the study design did not permit re-evaluation of sustained self-efficacy or an actual impact on deprescribing. Whiteboard videos can increase healthcare provider self-efficacy with deprescribing common PIMs. As a next step, we welcome a larger collaborative study to evaluate whether improved self-efficacy from our whiteboard videos translates to clinical practice behavior change. Justin P. Turner and Camille L. Gagnon had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Justin P. Turner: Concept and design, analysis, interpretation of data, preparation of manuscript. Camille L. Gagnon, Justin P. Turner: Concept and design, analysis, interpretation of data, preparation of manuscript. B. Ninh Khuong: Interpretation of data, preparation of manuscript. Emily G. McDonald: Interpretation of data, review and final approval of manuscript. Cara Tannenbaum: Concept and design, interpretation of data, review and final approval of manuscript. The authors would like to thank the members of the Canadian Medication Appropriateness and Deprescribing Network Healthcare Provider Committee for reviewing and providing feedback on the scripting and images during the creation and revision of the whiteboard videos. We would like to thank all the people, organizations and international deprescribing networks who shared the invitation to participate in the study because without their help we would not have achieved such an international response. Last, and by no means least, we would also like to thank Ms. Carole Alalouf, President and Founder of Exaltus, for her creative genius, dedication, and expertise in creating the whiteboard videos that brought our ideas to life. Open access publishing facilitated by Monash University, as part of the Wiley - Monash University agreement via the Council of Australian University Librarians. The authors have no financial or other conflicts with this manuscript. Justin P. Turner was funded through a postdoctoral fellowship through the Mitacs Accelerate program. All authors declare no support from any organization for the submitted work, no other financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work. The sponsors were not involved in the design, methods, data collection, or analysis of the study and had no role in the preparation of the manuscript. This study was funded by a Partnership for Health System Improvement Grant from the Canadian Institutes of Health Research (201410PHEPHE-337814-96399) and a National Chair Award from the Fonds de Recherche de Santé du Québec (2016/2017-33087). Additionally, Justin P. Turner was funded through a MITACS Elevate Postdoctoral Fellowship (IT11649). Table S1. Comparison of the mean self-efficacy scores on pre-tests versus post-tests for educational whiteboard videos. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.430
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations6
Published2023
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