A new hire support program for mental health occupational therapists: preventing burnout and building resilience
Bibliographic record
Abstract
Burnout is widespread among mental health clinicians, including Occupational Therapists (Morse et al., 2012; Scalan & Hazelton, 2019). Newer mental health clinicians tend to be at higher risk of burnout than experienced clinicians (McCombie & Antanavage, 2017). This risk of burnout has been heightened during the recent COVID-19 pandemic, as demands for mental health services in Canada have increased and healthcare staffing shortages have reached critical levels (Statistics Canada, 2022a; Statistics Canada 2022b). There are multiple factors that contribute to increased burnout for mental health OTs, including the demands of the job, nature of the work, lack of rewards, limited opportunities for training, resource shortages and decreased professional identity/discipline-specific supports (Abendstern et al., 2017; Devery et al., 2018; Gupta et al., 2012; Lloyd et al., 2005; Scanlan & Still, 2013). Burnout prevention literature, though limited, indicates that a multi-pronged approach can be helpful (Morse et al., 2012). The New Hire Support Program for Mental Health OTs provides a multi-intervention approach to help reduce burnout risk and bolster professional resilience for OTs who are new to mental health. This supportive, comprehensive program involves three evidence-based components: i) a resource support toolkit; ii) professional development and self-care plans; iii) a mentorship program. This program is positioned to not only directly address the issue of burnout and resilience for mental health OTs, but is also projected to have an important impact on retention rates and patient care. It will also add to a limited body of existing literature focused on clinician burnout prevention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".