Advancing the Recovery Orientation of Hospital Care Through Staff Engagement With Former Clients of Inpatient Units
Bibliographic record
Abstract
OBJECTIVES: This study was undertaken to assess the impact of consumer narratives on the recovery orientation and job satisfaction of service providers on inpatient wards that focus on the treatment of schizophrenia. It was developed to address the paucity of literature and service development tools that address advancing the recovery model of care in inpatient contexts. METHODS: A mixed-methods design was used. Six inpatient units in a large urban psychiatric facility were paired on the basis of characteristic length of stay, and one unit from each pair was assigned to the intervention. The intervention was a series of talks (N=58) to inpatient staff by 12 former patients; the talks were provided approximately biweekly between May 2011 and May 2012. Self-report measures completed by staff before and after the intervention assessed knowledge and attitudes regarding the recovery model, the delivery of recovery-oriented care at a unit level, and job satisfaction. In addition, focus groups for unit staff and individual interviews with the speakers were conducted after the speaker series had ended. RESULTS: The hypothesis that the speaker series would have an impact on the attitudes and knowledge of staff with respect to the recovery model was supported. This finding was evident from both quantitative and qualitative data. No impact was observed for recovery orientation of care at the unit level or for job satisfaction. CONCLUSIONS: Although this engagement strategy demonstrated an impact, more substantial change in inpatient practices likely requires a broader set of strategies that address skill levels and accountability.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".