Development of a recovery education program for inpatient mental health providers.
Bibliographic record
Abstract
OBJECTIVE: Mental health system transformation toward a recovery-orientation has created a demand for education to equip providers with recovery competencies. This report describes the development of a recovery education program designed specifically for inpatient providers. METHOD: Part 1 of the education is a self-learning program introducing recovery concepts and a recovery competency framework; Part 2 is a group-learning program focusing on real-life dilemmas and applying the Appreciative Inquiry approach to address these clinical dilemmas. A pilot study with a pretest/posttest design was used to evaluate the program. Participants included 26 inpatient multidisciplinary providers from 3 hospitals. RESULTS: The results showed participants' improvement on recovery knowledge (z = -2.55, p = .011) after the self-learning program. Evaluations of the group-learning program were high (4.21 out of 5). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: These results support continued efforts to refine the program. Inpatient providers could use this program to lead interprofessional practice in promoting recovery.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".