An Ecological Model for High-Risk Professional Decision-Making in Mental Health: International Perspectives
Bibliographic record
Abstract
Mental health professionals are frequently presented with situations in which they must assess the risk that a client will cause harm to themselves or others. Troublingly, however, predictions of risk are remarkably inaccurate even when made by those who are highly skilled and highly trained. Consequently, many jurisdictions have moved to impose standardized decision-making tools aimed at improving outcomes. Using a decision-making ecology framework, this conceptual paper presents research on professional decision-making in situations of risk, using qualitative, survey, and experimental designs conducted in three countries. Results reveal that while risk assessment tools focus on client factors that contribute to the risk of harm to self or others, the nature of professional decision-making is far more complex. That is, the manner in which professionals interpret and describe features of the client and their situation, is influenced by the worker’s own personal and professional experiences, and the organizational and societal context in which they are located. Although part of the rationale of standardized approaches is to reduce complexity, our collective work demonstrates that the power of personal and social processes to shape decision-making often overwhelm the intention to simplify and standardize. Implications for policy and practice are discussed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".