Bibliographic record
Abstract
Clinical assessment and treatment decision-making is a complex, everyday task for the substance use workforce. This Canadian study conducted with community substance use providers in the Interior region of British Columbia examines the factors clinicians pay attention to in their decision-making. A randomized factorial survey approach, using three unique vignettes embedded with factors of interest, was used to test the effect of case and respondent factors on assessment and treatment decisions. Responses were received from 106 participants, representing approximately a 35% response rate, yielding a sample size (n) of 308 vignettes. Multiple regression tested the independent effects of the vignette and clinician factors on assessment and treatment decisions. Factors within the vignette associated with withdrawal, physical illness and mental health issues emerged as the most predictive elements. The social complexity of people’s lives, client’s stated treatment preferences and readiness for change, and respondent characteristics were obscured in decision-making. This study indicates a lack of fidelity in the use of core assessment and treatment-matching tools, suggesting that clinician decision-making may, in everyday practice, be more heuristic and evidence-informed than evidence-based. Further research on normative decision-making practices in the substance-field is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.199 | 0.595 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".