Threshold Decisions in Social Work: Using Theory to Support Practice
Bibliographic record
Abstract
Abstract Decision making is an intrinsic and complex aspect of social work practice, requiring consideration of diverse but connected aspects. Decisions are often required as to whether a situation requires protective state intervention or whether it reaches the criteria for public or charitable services. Such instances of deciding whether or not a situation is ‘on one side of the line or the other’ are referred to in this article as ‘threshold judgements’. This article draws on experiences and material from a range of social work contexts to explore generalisable theory-informed understandings of ‘threshold judgements’ and ‘threshold decisions’ to develop knowledge and skills on this topic. The article outlines signal detection theory and evidence accumulation (‘tipping point’) theory and discusses these as ways to understand the key concepts underpinning threshold decisions in social work. We then argue that although these threshold concepts are a necessary part of decision making in social work, as in many other aspects of life, they are not sufficient. Operationalising such decisions requires some form of sense-making. Naturalistic decision making and heuristic models of judgement are discussed as frameworks for practice which seem to be useful in this context.
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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 | 0.068 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.006 | 0.071 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".