Understanding advocacy in action: A qualitative study
Bibliographic record
Abstract
Introduction Occupational therapists advocate with/for people with disabilities. In the occupational therapy literature, advocacy is mentioned within the context of specific populations or practice settings and thus there is no consistent way of describing advocacy itself. The objective of this article is to describe advocacy in action for occupational therapists who report engaging in advocacy with/for people with disabilities. Method In this hermeneutic phenomenological study, 13 occupational therapists were interviewed about their advocacy experiences. Data analysis was completed using a Gadamerian-based approach. Findings Advocacy is complex given that the specific tasks, the third party to whom advocacy is directed and the individual(s) advocating with the therapist vary greatly. Many skills used for advocacy are already part of the therapist’s practice, such as defining the problem, acquiring information, communicating and providing education. In this study, occupational therapists describe advocating as assisting clients who are struggling with access to equipment, services or funding. Also, they advocate by fostering the development of self-advocacy, rather than representing people with disabilities on an ongoing basis. Conclusion While these findings provide some insights about how advocacy is currently practiced, a detailed framework is needed to further guide therapists through this complex area of practice.
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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 | 0.036 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".