Evaluation of the sustained implementation of a mental health learning initiative in long-term care
Bibliographic record
Abstract
BACKGROUND: This paper describes an innovative education program for the management of mental health problems in long-term care (LTC) homes and the evaluation of its longer-term sustainability. Since 1998, the "Putting the P.I.E.C.E.S. Together" learning initiative has been providing education sessions and related learning strategies aimed at developing the knowledge and skills of health professionals who care for older persons with complex physical and mental health needs and associated behaviors, in Ontario, Canada. A major focus of this province-wide initiative was the development of in-house Psychogeriatric Resource Persons (PRPs). Evaluation of this initiative included the completion of pre- and post-education questionnaires (over three data collection time periods) assessing learner confidence (N = 1,024 and 792, for pre- and post-education, respectively) and session evaluation questionnaires gathering feedback on the session (N = 2,029 across all sessions). A survey of LTC homes in Ontario (N = 439, 79% of the homes in the province) was conducted to assess longer-term sustainability. RESULTS: Ratings of the sessions indicated that they were relevant to learners' clinical practice. There were significant increases in ratings of ability to recognize and understand challenging behaviors and mental health problems, and in ability to use a variety of assessment tools. Few homes (15%) do not have a PRP; over 50% of the staff who completed the first session in 1999 continue to serve as a PRP and to apply learned skills. CONCLUSIONS: A learning initiative with supportive and reinforcing strategies can develop in-house PRPs to enhance the care of the elderly in LTC. Incorporation of PRP functions into job descriptions and management support contributed to the success of this initiative. This study highlights the importance of work environments that support and reinforce the use of learned skills to the success of continuing education and quality improvement initiatives in LTC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".