Predictors of worklife burnout among mental health certified peer specialists.
Bibliographic record
Abstract
Certified peer specialists (CPSs) are a growing workforce that uses their lived experience of a behavioral health disorders plus skills learned in formal training to deliver support services. Despite their important role in the mental health care system and research on their working conditions, experiences of burnout have not been widely studied among CPSs. This study uses survey data from CPSs who were currently employed in any type of job, providing peer support services or not. Using the Maslach Burnout Inventory (MBI) and Areas of Worklife Survey (AWS), along with other measures of personal and job characteristics, relationships of predictors variables to burnout measures were described in unadjusted and adjusted linear regression models. Scores on each of the averaged burnout measures differed significantly between those employed in peer services jobs and those in other job types, with those in peer services jobs reporting lower exhaustion, cynicism, and higher professional efficacy. Better workload and fairness were associated with significantly lower exhaustion, and better reward and community were both associated with significantly lower cynicism. Those employed in peer services jobs had fewer signs of burnout than those in other occupations, in keeping with prior research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".