Caring for Physicians and other Healthcare Professionals: Needs Assessments for eCurricula on Physician and Workplace Health
Bibliographic record
Abstract
Abstract—The quality and sustainability of the healthcare system in Canada is dependent on the healthcare providers within it. If the system is to remain strong, it is critical that those who provide the services within it are strong and healthy. Unfortunately, downsizing in the Canadian healthcare system has led to extremely heavy workloads and high levels of burnout in healthcare providers, which not only affects their own health but also that of the patients they care for. In an attempt to provide support and resources for healthcare providers to improve their own health and well-being, the purpose of this project is to develop two online programs—one for physicians and medical students and one for other healthcare providers—that will (1) provide access to cutting-edge information related to health and wellness, (2) allow the users to evaluate their current fund of knowledge and health status and take action to improve it, and (3) direct the user to online and face-to-face resources and supports. The first step in the project involved identifying the needs of the target users for the two programs. This paper summarises the findings from these needs assessments and provides recommendations for program design and development. Keywords—physician health, workplace health, needs assessment, eLearning
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".