Improving Professional Decision Making in Situations of Risk and Uncertainty: A Pilot Intervention
Bibliographic record
Abstract
Abstract Social workers and other professionals providing mental health services are regularly required to make high-stakes decisions in situations characterised by conflicting demands. To better understand the factors that drive clinical decision making in situations of risk and uncertainty, we used a design-based research framework to pilot a new approach for improving professional decision making. The programme, which combined simulated interviews, a master class series and personal monitoring of real-time decisions, was designed to focus explicit attention on biological, emotional, cognitive and contextual influences on decision making. Preliminary results from a pilot study suggest that during and immediately following the intervention, clinicians demonstrated new insights into their decision making processes. In addition, they reported benefitting both from the opportunity to reflect individually and share reflections with others. Physiological data demonstrated an association between stressful decisions in real-world clinical practice, elevated heart rate and emotional responses. Qualitative data suggested that client risk represented only one aspect of decision making that resulted in emotional and physical responses, and others included team dynamics, socio-evaluative stressors and organisational and societal factors. This innovative decision making programme creates new opportunities for integrating research, practice and education and shows promise of improving social work practice.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".