Applicability and Clinical Utility of the Client-Centred Strategies Framework
Bibliographic record
Abstract
BACKGROUND: The Client-centred Strategies Framework consists of strategies for facilitating therapists' application of client-centred approaches. PURPOSE: The purpose of this study was to explore the application of the strategies and the utility of the framework to implement client-centred approaches. METHODS: The study used a sequential mixed-methods procedure. The quantitative phase consisted of a survey of 230 Canadian occupational therapists. The qualitative phase consisted of telephone focus groups with a sample of 14 Canadian respondents to the initial survey. FINDINGS: Results indicated that occupational therapists experience challenges in implementing strategies, particularly related to community organizing, coalition advocacy, and political action. Therapists identified multiple factors that influenced the implementation of strategies and ways of incorporating strategies into daily practice. The Client-centred Strategies Framework was viewed as a useful tool for increasing dialogue about occupational therapists' role in client-centred practice. IMPLICATIONS: The results of this study encourage an expanded view of client-centred strategies and the application of strategies to daily practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".