Dilemmas when working with substance abusers with multiple and complex problems: the case manager's perspective
Bibliographic record
Abstract
Since the 1990s, case management has been implemented in the USA and Canada – and recently also in various European countries – to support substance abusers with multiple and complex needs. Although this intervention is often presented as a set of standardised functions, its application is often a subjective task involving various dilemmas, which may influence case management outcomes significantly. Based on a comparison of case managers’ experiences in Denmark and Belgium, we focus on several core dilemmas in case management for substance abusers with complex problems. Case management practices vary from one project to the next and even within the same project. Such differences are apparently related to the way in which case managers approach dilemmas such as those existing between control versus self‐determination, or between systematic versus ad‐hoc planning. The conclusion is that it is vital to discuss these dilemmas during training courses and supervision meetings in order to ensure that the intended form of intervention is actually delivered on the ground.
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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 | 0.050 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".