Toward the reconceptualization of the relationship between occupation and health and well-being
Bibliographic record
Abstract
BACKGROUND: Foundational to the occupational therapy profession is the belief that engagement in occupation is health promoting; however, this belief fails to account for occupational engagement that may be risky or illness producing. Consensus regarding the nature of the relationship between occupation and health has yet to be achieved. PURPOSE: The purpose of this study is to provide a comprehensive description of how the relationship between occupation and health and well-being is discussed within the occupational therapy and occupational science literature. METHOD: The methodological framework outlined by Arksey and O'Malley served as the basis for this scoping review of the occupational therapy and occupational science literature. FINDINGS: One hundred and twelve articles were identified as meeting the criteria for inclusion. The dominant discourse portrays the relationship between occupation and health as positive. IMPLICATIONS: The broader literature suggests that occupational engagement can have both positive and negative effects on health and well-being. As such, the reconceptualization of the relationship between occupation and health and well-being is warranted to enable occupational therapists to practise in a more client-centred manner.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".