Psychological Capital Qualities and Psychological Well-Being in Australian Mental Health Professionals
Bibliographic record
Abstract
The mental health of mental health professionals has not been studied in detail to date, yet the work is stressful and many have left the field. What are the positive qualities that help mental health workers cope with their work and what pressures do they face? The purpose of the current study was to examine the psychological qualities and experiences of 56 Australian mental health professionals and compare these qualities with those of a general working group sample of 78 respondents, in regard to the similarities and differences demonstrated in psychological capital, positive psychological well-being, coping strategies, and mental health (depression, anxiety and stress) characteristics. Results from our online survey showed that the Australian mental health workers in our sample scored significantly higher on positive psychological capital attributes of optimism and goal-directed hope; significantly higher on psychological well-being (especially in valuing personal growth, and environmental mastery); and they scored significantly higher in the ability to use emotional coping effectively. They scored similarly to the general workplace sample on the depression, anxiety and stress scales; and similarly on active coping strategies. Conclusions are that those mental health workers continuing in the profession generally have high psychological well-being, provide a positive environment for their clients through their “psychological capital” emphasising optimism and hope, and they deal with their own pressures through positive emotional coping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".