Psychometric testing of a self-report measure of engagement in productive occupations
Bibliographic record
Abstract
BACKGROUND: Occupational therapists working with clients in productive occupations explicitly or implicitly assess their clients' occupational engagement. PURPOSE: To investigate the psychometric properties of the Profiles of Occupational Engagement in People with Severe Mental Illness: Productive Occupations (POES-P) in terms of internal consistency, initial construct validation, and floor and ceiling effects. METHOD: Participants (n = 93) from six day centres completed the data collection. Correlations between the POES-P and instruments measuring similar and dissimilar attributes, such as satisfaction, psychosocial functioning, and unmet needs, were studied. FINDINGS: A moderate relationship was found between the POES-P and occupational satisfaction (r(s) = 0.43) and a weak one with psychosocial functioning (r(s) = 0.22). The association with researcher-assessed participant engagement was slightly higher (r(s) = 0.37), and the relationship with unmet needs was nonsignificant (r(s) = -0.15). Internal consistency of the POES-P (alpha = 0.85) was good, but the distribution of responses indicated a ceiling effect. IMPLICATIONS: The POES-P seems promising for assessing engagement in work-like occupations but would benefit from further development.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".