Substance use and meaning: transforming occupational participation and experience
Bibliographic record
Abstract
Abstract Introduction We draw on activity theory of concepts to examine ‘meaning of occupation’ and ‘substance use’ beyond preconceived notions of inherent positive or negative experiences. Objective To explore nuanced meanings of substance use and associated occupations. Method An online survey and semi-structured interviews were used to collect data from professionals about prevalence of substance use, substance effects, and personal experiences. In analyzing the interview data, we attended to substance use as a discrete occupation in itself, substance use co-occurring with other occupations, and substance use altering the performance, participation, and experience of occupations. Results Three broad themes related to meaning: i) complex meanings attributed to substance use, ii) meanings of substance use as shifting and variable, and iii) meanings of substance use in the context of other occupations. Substance use enhances occupations, transforms meaning of occupations, and mitigates less desired aspects of occupations. Work, construed as positively meaningful and valued in occupational therapy literature, was a source of stress, unhappiness, and worry; substance use facilitated relaxation and pleasure. Conclusion This study furthers occupational therapy knowledge with respect to implications for conceptualization that extend beyond dualist framings and implications for occupational therapy education, practice, and policy.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".