A framework for occupational enablement to facilitate social change in community practice
Bibliographic record
Abstract
BACKGROUND.: Community practice in occupational therapy is becoming increasingly relevant worldwide. Moreover, a social-change approach focusing on occupational enablement is pertinent in community practice as occupational therapists endeavour to promote health, well-being, and occupational justice in communities. PURPOSE.: Drawing on theory from the fields of community development, community engagement, and occupational enablement, and based on the findings of a previous empirical, qualitative study, a framework for occupational enablement in community practice was developed. KEY ISSUES.: This article presents the background to the development of the framework, after which the framework is introduced and discussed in terms of the composition, relevance, and application of its components. The framework details outcomes and objectives that may be targeted and activities that may be utilized successfully during occupational therapy community practice engagements. It further illuminates facilitators of enablement related to contextual factors, stakeholders, and strategies that enhance the potential for enabling community practice engagements. IMPLICATIONS.: This framework can provide a strategic management guideline for occupational therapists and students who engage with communities in endeavours such as community development and service learning.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".