Food skills group value, meaning, and use with inpatients in a mental health setting
Bibliographic record
Abstract
Introduction Occupational therapists use food skills groups to support mental health recovery and engagement in meaningful occupation. Research examining participants’ experiences in these mental health groups is sparse. Method Researchers used a convergent parallel mixed methods design. Following participation in an inpatient food skills group, 60 participants completed a questionnaire, and 20 also participated in a semi-structured interview. The questionnaire data was analysed using descriptive and correlational statistics. The interview data was analysed using thematic analysis. Triangulation was used to combine and interpret findings from the different methods to gain a more complete understanding of the participants’ experiences. Findings Participants strongly agreed/agreed that they enjoyed participating, felt having the opportunity to participate was important to their recovery, and after discharge would use the skills they had learnt. Age, sex, and level of education were associated with some of the questionnaire responses. Three main themes emerged from the interview data: importance of engaging/doing, connections with food, and being involved in a group process. Conclusion Individuals who participated in the food skills group valued having the opportunity to build skills and engage in meaningful activities with others while in hospital. They identified the food skills group as important to their recovery.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".