Challenges in enacting occupation-based social transformative practices: A critical dialogical study
Bibliographic record
Abstract
BACKGROUND.: Globally, occupational therapists are taking up the transformative potential of occupation to mobilize the profession's commitment to social change. PURPOSE.: This study examined ideal constructions of occupation-based social transformative practices and challenges that may arise when enacting these practices. METHOD.: Five participants with experiences developing practices aligned with social transformative goals in diverse locations were recruited. In this critical dialogical study, three dialogical interviews were conducted with each participant. Critical reflexivity was enacted through the exchange of transcripts and critical reflections with participants. A critical discourse analysis was conducted to examine how such practices are shaped within discourses and other contextual features. FINDINGS.: The findings address constructions of ideal practice and three threads that provide critical insights into ways discourses shape possibilities to enact social transformation through occupation. IMPLICATIONS.: This study brings together experiential and theoretical knowledge to advance social transformative practices by problematizing underexamined discourses in occupational therapy.
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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.032 | 0.054 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".