A global profile of occupational therapy and primary care: Results from the World Federation of Occupational Therapists survey
Bibliographic record
Abstract
Background: Despite the clearly identified roles and need for occupational therapy in primary care, guidance on a consistent global response is lacking. Purpose: This study aimed to gain insight into the global state of occupational therapy primary care services. Method: Data from an online survey of the 100 WFOT member organisations was grouped by country income levels; weighted global means and ANOVA were calculated. Results: Fifty-nine member organisations from five world regions responded, 33 (55.9%) representing high-income countries. Activities of daily living was the most addressed area; other health facilities (e.g., mental health services, rehabilitation services) were reported as a common setting. People with developmental disabilities, those with chronic illness/needs, and older adults were most likely to receive occupational therapy primary care services, with a higher likelihood of being financed by government (public) in one-on-one settings. Workforce supply, funding and training were consistently reported as key limiting factors for the profession in primary care. Implications: While recognizing regional histories and needs, it behooves the profession to work collectively to advance its capacity to provide primary care services. Key enablers to advance this area include workforce initiatives and funding access in line with a global workforce strategy for occupational therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".