Letter to the editor
Bibliographic record
Abstract
In Canada, primary care reform has focused on the need for team-based models of service delivery. There is need to ensure that clinicians as well as students in health professional programs are prepared for team-based primary care practice.Our project aimed to develop online modules to enhance the capacity of occupational therapy learners and practitioners to practice in primary care teams across the country.The first module used a consensus methodology with representatives from physiotherapy, social work, speech and language, audiology and dietitians to identify foundational knowledge on primary care. A pan-Canadian team, supported by the Canadian Association of Occupational Therapists (CAOT) and a community advisory group, developed the second module, focused on occupational therapy roles in primary care. An evaluation framework was developed, and modules were piloted in two Canadian occupational therapy programs.Through a collaborative, interprofessional process we created two evidence-based online modules. The first, an introduction to primary care, addresses the values and principles of collaborative primary care and models of team-based primary care. The second focuses on how to integrate and work in primary care teams using the occupational therapist’s unique lens including assessments, interventions and a focus on equity and access. Pilot study results support the value of both modules.Evidence-based on-line modules will be available to occupational therapists and entry-level university programs.Occupational therapists globally may benefit from the modules, as more occupational therapy positions are approved in primary care settings.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".