Examining the intersection of policy and occupational therapy: A scoping review
Bibliographic record
Abstract
BACKGROUND.: Policy has direct bearing on occupational therapy practice and is a key dimension of the environment. To date, there has not been a synthesis of how policy is addressed in the occupational therapy literature. PURPOSE.: The purpose of this review is to identify and analyze the ways that policy is addressed in the occupational therapy literature. METHOD.: We conducted a scoping review of literature on policy and occupational therapy. Articles were analyzed deductively using a policy framework and inductively to identify overarching themes. FINDINGS.: Sixty-four papers were included for analysis. We present the descriptive information of the included articles according to policy type, sphere of action, stage of policy development, level, and actors. The qualitative analysis uncovered two broad concepts: "awareness" about the relationship between policy and practice and messages pertaining to "action." IMPLICATIONS.: There are numerous areas for further research into the policy-occupational therapy nexus, as policy has numerous pathways to shape practice. Practitioners will benefit from better understanding these pathways and actively engaging to align policy with the values of the profession.
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 | 0.021 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.032 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".