Professional development perceptions and activities of psychiatrists and mental health nurses in New Zealand.
Bibliographic record
Abstract
AIMS: Psychiatrists (n=26) and mental health nurses (n=18) engaged in the practice of psychotherapy were surveyed regarding their perceptions and engagement in professional development activities. METHODS: Collaborative Research Network's (CRN) methodology was followed, and comparisons with CRN samples from Canada and the United States of America (USA) were undertaken. RESULTS: New Zealand psychiatrists reported perceived development across their careers, but their ratings were lower than those of nurses. Both professional groups rated their overall development lower their Canadian counterparts. However, New Zealand nurses reported more involvement in supervision than psychiatrists, and both groups reported rates that exceeded those reported in Canadian and USA samples. New Zealand subgroups reported low involvement in personal therapy in comparison to overseas samples. Supervision and personal therapy were highly regarded by New Zealand practitioners, but didactic training was rated as less important. CONCLUSIONS: New Zealand mental health professionals reported attainment of therapeutic mastery and skill acquisition. New Zealand psychiatrists reported less involvement in case supervision, but rated supervision as having the greatest influence to their development. The results highlight areas of need for continuing professional development for these professions.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".