Leadership coaching transforming mental health systems from the inside out: The Collaborative Recovery Model as person-centred strengths based coaching psychology
Bibliographic record
Abstract
Mental health service provision is being transformed by a call for ‘recovery oriented care’. Rather than the traditional medical meaning of cure, the term ‘recovery’ refers to the personal and transformational process of patients living with mental illness, moving towards a preferred identity and a life of meaning – a framework where growth is possible, and the fixed mindsets around diagnoses such as schizophrenia are challenged. At an organisational level, however, organisations and their service providers have typically operated on a framework that is fixed in terms of the potentialities of the mental health patients. This paper describes the ongoing transformation of a large tertiary inpatient mental health unit in Ontario, Canada, through a parallel staff and patient implementation of a person-centred strengths based coaching framework, known as the Collaborative Recovery Model (CRM). Consistent with developments in positive psychology, the model focuses on strengths and values, goals and actions, within a coaching framework, with an emphasis on the alliance between staff and patient, and the growth potential of the patient. By using the principles of coaching psychology, mental health staff members are leading change in the organisation by personal use of the principles and practices that they are also using to coach patients. The leadership and organisational change challenges are described and future directions are discussed.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".