Conocimiento, aportación y aplicación de los marcos teóricos de los terapeutas ocupacionales que trabajan en España
Bibliographic record
Abstract
OBJECTIVE the main objective of this paper is to show the degree of knowledge, contribution and application of existing theoretical frameworks in the international arena by occupational therapists working in our country.\nMETHODOLOGY quantitative research based in a descriptive cross-sectional study. The used skill was a designed questionnaire of express form for this study. Spread online between April 1 and May 15, 2013 to a sample of occupational therapists of Spain which fundamental professional work was the clinic. \nRESULTS the theoretical framework best known by the subjects of the study is the model of human occupation with 57,4% and this same theoretical framework is the one from which they gain the most important contribution with a further 38,9%. On the other hand, the rehabilitation referral or compensator framework is the most employed (29,9%).\nCONCLUSIONS the model of human occupation is the theoretical framework about which more respondents know about and the one that provides them more. The Canadian model of occupational performance is the second theoretical framework best known by the subjects of the sample. The frame of reference or rehabilitative compensator is the theoretical framework more applied by the respondents, and the second that provides most to the sample. Overall, knowledge of the theoretical frameworks by respondents is average, the contribution of these frameworks is low with very low application of them.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".