Occupational The. \t sts, Assessment Practices Handicapped i Ch il en
Bibliographic record
Abstract
A survey was conducted to identify which approaches and methods of assessment are used by Ontario occupational therapists working with handicapped children; how fa-miliar therapists are with specific aspects of some methods used, how satisfied they are with these meth-ods, and what assessment goals therapists consider important. A questionnaire was mailed to 99 sources which included hospitals, re-habilitation centres, school boards, daycare centres, and private practi-tioners. Sixty-nine (70%) occupational therapists comprised the sample. The prototypical occupational therapist surveyed has an undergraduate university degree, works full-time as a staff therapist in a rehabilitation centre or hospital with neurologically handicapped children, and has worked for more than five years in the field of pedi-atrics. Frequently used assessment methods are standardized tests and observational tools such as check-lists, rating scales, and anecdotal reports. The majority of respondents who use standardized tests are satis-fied with them. The aspects of the tests most familiar to the respon-dents are scoring and administra-tion procedures as compared to reli-ability and validity information. The majority of respondents who use tests, conduct test evaluation proce-dures. The results are discussed with their implications for training, prac-tice and further research in occupa-tional therapy. Pediatric occupational therapy prac-tice reveals that occupational thera-pists have specialized knowledge and experiences in working with handicapped children (Ayres, 1979;
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".