The clinical effects of individual career counseling on clients’ psychological distress
Bibliographic record
Abstract
Abstract This study examined the clinical effects of career counseling on psychological distress and the role of counselor adherence, working alliance, and client neuroticism in predicting these effects. The 239 participants received an average of 7.81 sessions at a university career counseling center. Among clients with a clinical level of psychological distress ( n = 179) at the study's inception, 55.87% recovered, 22.35% improved, 19.55% experienced no change, and 2.23% saw an aggravation of their psychological distress. Results showed that a higher level of counselor adherence to the intervention manual significantly increased the probability that clients recovered or improved as compared to not experiencing significant change. Working alliance did not predict clinical change, nor did it moderate the effect of counselor adherence. Clients who improved had higher levels of neuroticism than clients who recovered.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Study of the clinical effects of career counseling on psychological distress; the object is counseling outcomes.
The study evaluates clinical effects of career counseling, not research itself.
Clinical outcomes of career counseling for client distress; counseling effectiveness, not research systems.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".