Conditions d’adoption du dossier de santé électronique personnel par les professionnels de la première ligne au Québec : perspectives professionnelle et organisationnelle
Bibliographic record
Abstract
INTRODUCTION: We aimed to describe and analyse the factors and conditions influencing ePHR adoption by primary healthcare professionals for the follow-up and management of chronic diseases, as perceived by healthcare professionals and health organization managers. METHODS: A qualitative study was conducted in the context of an ePHR experimentation project in Quebec. In-depth semi-structured individual interviews were conducted with 11 professionals and three managers directly involved in ePHR implementation in a primary healthcare organization. RESULTS: The results highlight the emergence of themes comprising facilitators or barriers to ePHR adoption. The main factors identified were the clinicians' leadership and previous involvement in organizational transformations, the context of practice, technology maturity providing a useful, additional and relevant content, integration with the available clinical information systems facilitating two-way communication and supporting the development of patient-professional partnerships and patients' use and adherence. The organizational precursors identified refer to the organizational receptivity to change, adjustment to participants' values, and the policies and practices set up to support ePHR adoption by professionals and their patients. Cost is a major issue determining ePHR implementation. CONCLUSION: The factors and conditions identified will be useful strategically and operationally to design and implement new clinical and organizational practices and develop adapted technologies facilitating ePHR adoption by professionals.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".