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Record W2006479550 · doi:10.2196/jmir.1983

Health Care Professionals’ Beliefs About Using Wiki-Based Reminders to Promote Best Practices in Trauma Care

2012· article· en· W2006479550 on OpenAlexafffundabout
Patrick Archambault, A Bilodeau, Marie‐Pierre Gagnon, Karine Aubin, André Lavoie, Jean Lapointe, Julien Poitras, Sylvain Croteau, Martin Pham-Dinh, France Légaré

Bibliographic record

VenueJournal of Medical Internet Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsInstitut National d'Excellence en Santé et en Services SociauxPublic Works and Government Services CanadaCentre hospitalier universitaire de QuébecUniversité LavalCentre intégré de santé et de services sociaux de Chaudière-Appalaches
FundersCanadian Health Services Research FoundationCanadian Institutes of Health ResearchUniversité Laval
KeywordsKnowledge translationHealth careHealth professionalsBest practiceNursingeHealthPsychologyMedicineMedical educationKnowledge managementComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Wikis are knowledge translation tools that could help health professionals implement best practices in acute care. Little is known about the factors influencing professionals' use of wikis. OBJECTIVES: To identify and compare the beliefs of emergency physicians (EPs) and allied health professionals (AHPs) about using a wiki-based reminder that promotes evidence-based care for traumatic brain injuries. METHODS: Drawing on the theory of planned behavior, we conducted semistructured interviews to elicit EPs' and AHPs' beliefs about using a wiki-based reminder. Previous studies suggested a sample of 25 EPs and 25 AHPs. We purposefully selected participants from three trauma centers in Quebec, Canada, to obtain a representative sample. Using univariate analyses, we assessed whether our participants' gender, age, and level of experience were similar to those of all eligible individuals. Participants viewed a video showing a clinician using a wiki-based reminder, and we interviewed participants about their behavioral, control, and normative beliefs-that is, what they saw as advantages, disadvantages, barriers, and facilitators to their use of a reminder, and how they felt important referents would perceive their use of a reminder. Two reviewers independently analyzed the content of the interview transcripts. We considered the 75% most frequently mentioned beliefs as salient. We retained some less frequently mentioned beliefs as well. RESULTS: Of 66 eligible EPs and 444 eligible AHPs, we invited 55 EPs and 39 AHPs to participate, and 25 EPs and 25 AHPs (15 nurses, 7 respiratory therapists, and 3 pharmacists) accepted. Participating AHPs had more experience than eligible AHPs (mean 14 vs 11 years; P = .04). We noted no other significant differences. Among EPs, the most frequently reported advantage of using a wiki-based reminder was that it refreshes the memory (n = 14); among AHPs, it was that it provides rapid access to protocols (n = 16). Only 2 EPs mentioned a disadvantage (the wiki added stress). The most frequently reported favorable referent was nurses for EPs (n = 16) and EPs for AHPs (n = 19). The most frequently reported unfavorable referents were people resistant to standardized care for EPs (n = 8) and people less comfortable with computers for AHPs (n = 11). The most frequent facilitator for EPs was ease of use (n = 19); for AHPs, it was having a bedside computer (n = 20). EPs' most frequently reported barrier was irregularly updated wiki-based reminders (n = 18); AHPs' was undetermined legal responsibility (n = 10). CONCLUSIONS: We identified EPs' and AHPs' salient beliefs about using a wiki-based reminder. We will draw on these beliefs to construct a questionnaire to measure the importance of these determinants to EPs' and AHPs' intention to use a wiki-based reminder promoting evidence-based care for traumatic brain injuries.

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)0.012
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.240
GPT teacher head0.601
Teacher spread0.361 · 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 designQualitative
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".

Quick stats

Citations36
Published2012
Admission routes3
Has abstractyes

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