A new hire support program for mental health occupational therapists: preventing burnout and building resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it