Modeling the mental health service utilization decisions of university undergraduates: A discrete choice conjoint experiment
Bibliographic record
Abstract
OBJECTIVE: We modeled design factors influencing the intent to use a university mental health service. PARTICIPANTS: Between November 2012 and October 2014, 909 undergraduates participated. METHOD: Using a discrete choice experiment, participants chose between hypothetical campus mental health services. RESULTS: Latent class analysis identified three segments. A Psychological/Psychiatric Service segment (45.5%) was most likely to contact campus health services delivered by psychologists or psychiatrists. An Alternative Service segment (39.3%) preferred to talk to peer-counselors who had experienced mental health problems. A Hesitant segment (15.2%) reported greater distress but seemed less intent on seeking help. They preferred services delivered by psychologists or psychiatrists. Simulations predicted that, rather than waiting for standard counseling, the Alternative Service segment would prefer immediate access to E-Mental health. The Usual Care and Hesitant segments would wait 6 months for standard counseling. CONCLUSIONS: E-Mental Health options could engage students who may not wait for standard services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".