Using intuition in social work decision making
Bibliographic record
Abstract
Social workers must make ‘justifiable’ decisions, but ‘intuition’ is also important in assessment, decision making and working with risk. We discuss intuition within professional judgement as being part of our cognitive faculties; emotionally-informed reasoning processes connecting workers with clients and families; and intuition making use of internalised learning. Challenges discussed include intuition as a taboo topic; communicating intuition-based judgements within group decision processes; and lack of models for integrating intuition with explicit use of knowledge. To develop the professional knowledge base on professional judgement, the paper considers six theoretical frameworks which might be used to conceptualise intuition within social work decision making, including: (1) the ‘tacit knowledge’ of sociological discourse; (2) intuition as ‘sense-making’; (3) internalisation of learning; (4) conceptual schemas from neuroscience; (5) Kahneman’s ‘thinking fast and slow’; and (6) decision heuristics. Intuition is discussed in the context of supervision and organisational governance; use of assessment tools and processes; creation of mental models for practice; implications for education and training; and further research. Although the profession must continue to develop its ability to use the best knowledge to inform practice, a psycho-social rationality model may be required to conceptualise internalised ‘intuitive’ judgement processes in practice.
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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 | 0.051 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".