P01-184 - Do I Really Matter to you? The Potential Influence of Social Desirability on Treatment Outcome
Bibliographic record
Abstract
Objectives Continuous client feedback aims to increase the effectiveness of therapy by emphasizing common factors in treatment across theories that contribute the most to change. In this paper, we present data using two independent measures, which appears to isolate the influence of social desirability on treatment outcomes. Methods Continuous client feedback data was collected in the Student Health Partnership program and compared with independent data reflecting client function (Child Global Assessment of Function). Data was collected at two times by the same staff using two sampling methods. In the first sample, staff preferentially assigned clients to case (continuous client feedback) and comparison treatment as usual) on the basis of preference and convenience whereas in the second sample assignment to case and comparison was random. Results When the data from the two sample frames was compared, systematically different trajectories in the measured outcomes reflecting continuous client feedback and function were obtained with those in sample one being substantially higher than comparisons or population reference values. Conclusions The difference in results emerging from the two sampling frames is explained in terms of social desirability. Outcomes for clients were much better in the first sample, where staff choose clients they apparently liked for specialized treatment.
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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 | 0.021 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".