Bibliographic record
Abstract
There are some puzzling questions about why occupational therapists in the United Kingdom (UK) appear reluctant to enter independent practice and why some colleagues in the statutory sector frown upon those of us who do. As a founding member of the specialist section for occupational therapists working in this sector, I have often reflected on why others find it so difficult to imagine working for themselves and being paid for their services. Before the establishment of the National Health Service, Dr Elizabeth Casson wrote about insurers being asked to fund rehabilitation so that occupational therapy could be more widely used to treat injured people. Casson (1941) also noted that, in the United States, occupational therapy was funded by insurance companies, who recognised that it reduced costs. If there was nothing wrong at that time in expecting an organisation to pay for treatment, why has the growth of the independent sector in this country been so slow? In other countries, independent practice is well established, is growing fast and forms part of a mixed economy of health care. In response to enquiries, the Canadian Association of Occupational Therapists’ membership statistics for 2009/2010 indicate that 21% of their members report self-employment as part of their primary, secondary or tertiary employment. Similarly, the Occupational Therapy Association of South Africa indicates that over 50% of its members currently registered work in private practice. However, the UK specialist section directory has only 200 members advertising their services out of a current section membership of over 500 and a British Association of Occupational Therapists’ membership of over 23,000 UK professional members.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".