From not knowing to doing: An interprofessional approach to clinician training in use of Goal Attainment Scaling (GAS) as a recovery‐oriented outcome measure in a rural mental health service
Bibliographic record
Abstract
Outcome measurement and feedback are key to quality improvement in healthcare. Goal attainment scaling (GAS) is a tool that could be used to measure outcomes of mental health services delivering recovery-oriented care. The objective of this prospective study was to evaluate the effectiveness of tailored, interprofessional, multilevel and adaptable GAS training on clinician views, learning, competence, performance and confidence in the use of GAS. Thematic analysis of eight clinician participant views was done using the method proposed by Braun and Clarke (Thematic analysis: a practical guide to understanding and doing, 2022). Four main themes were generated: clinicians found that this type of training is useful, GAS influenced the way they thought about their roles in goal setting and recovery-oriented care and COVID-19 pandemic impacts. Furthermore, clinicians' skills to set scalable GAS goals with consumers and clinician confidence in using GAS improved. The results of this study show a positive impact of tailored, interprofessional, multilevel and adaptable training supporting development of clinician skills in the GAS process. The training design had a favourable effect on clinician views, learning, competence, performance and confidence of GAS as a recovery-oriented outcome measure. The approach to GAS training and use of GAS as a recovery-oriented outcome measure should be considered in response to mental health service reform.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".