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Record W2896853792 · doi:10.1186/s12913-018-3587-7

Engaging patients and primary care providers in the design of novel opinion leader based interventions for acute asthma in the emergency department: a mixed methods study

2018· article· en· W2896853792 on OpenAlexafffund
Cristina Villa‐Roel, Maria B. Ospina, Sumit R. Majumdar, S. Couperthwaite, Erin Rawe, Taylor Nikel, Brian H. Rowe

Bibliographic record

VenueBMC Health Services Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsMedicineEmergency departmentPsychological interventionFocus groupGuidelineHealth informaticsAsthmaFamily medicineHealth administrationNursingPublic healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Multifaceted interventions driven by the needs of patients and providers can help move evidence into practice more rapidly. This study engaged both patients and primary care providers (PCPs) to help design novel opinion leader (OL)-based interventions for patients with acute asthma seen in emergency departments (EDs). METHODS: A mixed methods design was employed. In phase I, we invited convenience samples of patients with asthma presenting to the ED and PCPs to participate in a survey. Perceptions with respect to: a) an ideal OL-profile for asthma guidance; and b) content, style and delivery methods of OL-based interventions in acute asthma directed from the ED were collected. In phase II, we conducted focus groups to further explore preferences and expectations for such interventions with attention to barriers and facilitators for implementation. RESULTS: Overall, 54 patients completed the survey; 39% preferred receiving guidance from a respirologist, 44% during their ED visit and 56% through individual discussions. In addition, 55% expressed interest in having PCP follow-up within a week of ED discharge. A respirologist was identified as the ideal OL-profile by 59% of the 39 responding PCPs. All expressed interest in receiving notification of their patients' ED presentation, most within a week and including diagnosis and ED/post ED-treatment. Personalized, guideline-based, recommendations were considered to be the ideal content by the majority; 39% requested this guidance through a pamphlet faxed to their offices. In the focus groups, patients and PCPs recognized the importance of health professional liaisons in transitions in care; patient anxiety and PCP time constraints were identified as potential barriers for ED-educational information uptake and proper post-ED follow-up, respectively. CONCLUSIONS: Engaging patients and PCPs yielded actionable information to tailor OL-based multifaceted interventions for acute asthma in the ED. We identified potential facilitators for the implementation of such interventions (e.g., patient interaction with alternative health care professionals who could facilitate transitions in asthma care between the ED and the primary care setting), and for the provision of post discharge self-management education (e.g., consideration of the first week of ED discharge as a practical time frame for this intervention). Prioritization of identified barriers (e.g., lack of PCP involvement) could be addressed by the identification of potential early adopters in practice environments (e.g., clinicians with special interest in asthma).

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)0.059
metaresearch head score (Gemma)0.053
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.053
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.491
GPT teacher head0.607
Teacher spread0.117 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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