Do we know when our clients get worse? an investigation of therapists' ability to detect negative client change
Bibliographic record
Abstract
Abstract Routine clinical judgment is often relied upon to detect client deterioration. How reliable are therapists' judgments of deterioration? Two related studies were conducted to investigate therapist detection of client deterioration and therapist treatment decisions in situations of deterioration. The first study examined therapists' ability to detect client deterioration through the review of therapy progress notes. Therapist treatment decisions in cases of client deterioration were also explored. Therapists had considerable difficulty recognizing client deterioration, challenging the assumption that routine clinical judgment is sufficient when attempting to detect client deterioration. A second study was a survey of therapists asking how they detect client deterioration and what treatment decisions they make in response. Symptom worsening was the most commonly stated cue of deterioration. Copyright © 2009 John Wiley & Sons, Ltd. Key Practitioner Message: • Clinicians may have a difficult time detecting when their client's symptoms are worsening. • Outcome assessment strategies do exist to help clinicians detect client deterioration.
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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 | 0.032 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".