Étude des déterminants individuels de l'adoption du dossier de santé électronique du Québec
Bibliographic record
Abstract
AIM: The potential of electronic health records to improve effectiveness, safety and quality of health care has been shown in several previous studies. However healthcare professionals remain reticent as for its use, which limits its potential effect on the health care system. The present study aimed to evaluate physicians' perceptions towards the electronic health record of Quebec. METHODS: Based on a literature review of the factors affecting the adoption of information and communication technologies in general, and e-health in particular, questionnaire was developed. A total of 12 doctors who represent potential users of the Quebec electronic health record completed and returned the questionnaire. Afterwards we performed a thematic analysis of content which was followed by a theorisation of emerging concepts. RESULTS: Physicians' intention to adopt the Quebec electronic health record is positively influenced by perceived usefulness, perceived ease of use, demonstrability of the results, system's compatibility with practice, and computer self-efficacy. Conversely, resistance to change negatively influences physicians' adoption of the electronic health record. CONCLUSION: It is crucial to understand factors that influence the acceptance of the Quebec electronic health records to inform decision makers. This will allow identifying potential users' expectations and to adjust implementation strategies accordingly in order to favour a better integration of this technology into medical practices.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".