Exploring Arising Moments and Mindfulness in Occupational Therapists Working in Diverse Clinical Practice Areas
Bibliographic record
Abstract
An online survey was conducted with 72 Canadian occupational therapists to (1) explore if and how occupational therapists were aware of "arising moments" in clinical practice, which are moments that give rise to emotions, sensations, and thoughts, and (2) to examine therapists' responses to questions related to mindfulness. Participants described arising moments through an open-ended survey question. Framework analysis was used to develop key themes and subthemes. A modified Philadelphia Mindfulness Awareness (PHLMS) subscale with two additional survey questions "understanding of living in the moment and being present, and awareness of mindfulness" (sum = PAM) were used as indicators of mindfulness. There were significant differences in the means of PHLMS mindfulness among therapists working in diverse practice areas (F = 3.63, p = .009). Posthoc analysis revealed that therapists working in mental health had higher mean PHLMS-mindfulness scores than in all other groups. There were no significant differences in PAM mindfulness among the practice areas (ANOVA, F = 2.15, p = .08). However, posthoc tests showed that the participants in the mental health practice area had a significant difference with one group, general physical health. Findings have implications for practice in occupational therapy and education about mindfulness in occupational therapy.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".