Investigating service features to sustain engagement in early intervention mental health services
Bibliographic record
Abstract
AIM: To understand what service features would sustain patient engagement in early intervention mental health treatment. METHODS: Mental health patients, family members of individuals with mental illness and mental health professionals completed a survey consisting of 18 choice tasks that involved 14 different service attributes. Preferences were ascertained using importance and utility scores. Latent class analysis revealed segments characterized by distinct preferences. Simulations were carried out to estimate utilization of hypothetical clinical services. RESULTS: Overall, 333 patients and family members and 183 professionals (N = 516) participated. Respondents were distributed between a Professional segment (53%) and a Patient segment (47%) that differed in a number of their preferences including for appointment times, individual vs group sessions and mode of after-hours support. Members of both segments shared preferences for many of the service attributes including having crisis support available 24 h per day, having a choice of different treatment modalities, being offered help for substance use problems and having a focus on improving symptoms rather than functioning. Simulations predicted that 60% of the Patient segment thought patients would remain engaged with a Hospital service, while 69% of the Professional segment thought patients would be most likely to remain engaged with an E-Health service. CONCLUSIONS: Patients, family members and professionals shared a number of preferences about what service characteristics will optimize patient engagement in early intervention services but diverged on others. Providing effective crisis support as well as a range of treatment options should be prioritized in the future design of early intervention services.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".