Improving Clinician Wellbeing in Mental Health Care
Bibliographic record
Abstract
Mental health issues among children and youth have steadily been on the rise in Canada. One of the ways that the Provincial Government of British Columbia addresses this issue is by employing mental health clinicians (MHCs) on various community mental health teams across the province. It is well established in the literature that community MHCs experience considerably high levels of occupational stress (OS) in their therapeutic roles (O’Connor et al., 2018). Chronic exposure to OS makes MHCs vulnerable to the compassion fatigue and burnout, which are considered occupational hazards (OHs) of mental health care (Bride et al., 2007; O’Connor et al., 2018). This organizational improvement plan (OIP) addresses the problem that MHCs at Strong Communities (SC), are insufficiently practicing stress-reducing behaviours, proportionate to the OS-levels that are typical in the field. This problem, and the vision for change, were explored through the lenses of critical theory and servant leadership. Organizational change readiness was assessed, and the Wellbeing Workout (WW) (Hughes et al., 2019) was chosen as the solution to the Problem of Practice. A change implementation plan was created to operationalize the WW as a team-based change initiative. This OIP is influenced by my position as an informal leader and a front-line MHC. I utilize Kouzes and Posner’s (2017) Five Practices of Exemplary Leadership, and manage change using Change Path Model (Deszca et al., 2020) and Prochaska and DiClemente’s (2005) Stages of Change model. The change initiative is evaluated using three Plan, Do, Study, Act cycles (Deming, 1994/2018). Although the primary goal of this change initiative will be to reduce OS and OHs for MHCs, the long-term ambition of the plan is for SC to be re-conceptualized as a vicarious trauma-informed organization.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".