Use of qualitative methods to optimize collaborative practices by highlighting differences in perceptions between professionals: an example of patient education
Bibliographic record
Abstract
Interprofessional working must be approached within health promotion interventions using systematic methods to identify areas of suboptimal collaboration. We designed a qualitative study with a purposive sample of seven French therapeutic patient education programs. Semi-structured individual interviews were conducted with 14 healthcare providers and seven clinician leaders (coordinators) involved in patient education. We used the same interview guide and thematic grid regardless of the professional's profile to compare their perceptions on elements affecting outcome, participation and sustainability of programs. Healthcare providers and coordinators addressed non-convergent issues at both ends of a continuum from a micro-level nested in the program delivery to a macro-level corresponding to the structured implementation and sustainability of the program. Meso-level issues featured convergent perspectives. Our methodology could be used at the level of health services in a health system to provide a complete recovery of stakeholders' perspectives (without "blind spots" from one stakeholder or another). In our study, we focused on patient education in the French health system and pointed out possible considerations to optimize the functioning of programs. Such considerations include specific training plan development, encouraging reflection on the content and use of initial assessment, leading sessions in pairs to save on work time, and communication on the ins and outs of organizational imperatives that require healthcare providers' contributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.105 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".