Preconsult interactive computer-assisted client assessment survey for common mental disorders in a community health centre: a randomized controlled trial
Bibliographic record
Abstract
<h3>Background:</h3> Access disparities for mental health care exist for vulnerable ethnocultural and immigrant groups. Community health centres that serve these groups could be supported further by interactive, computer-based, self-assessments. <h3>Methods:</h3> An interactive computer-assisted client assessment survey (iCCAS) tool was developed for preconsult assessment of common mental disorders (using the Patient Health Questionnaire [PHQ-9], Generalized Anxiety Disorder 7-item [GAD-7] scale, Primary Care Post-traumatic Stress Disorder [PTSD-PC] screen and CAGE [concern/cut-down, anger, guilt and eye-opener] questionnaire), with point-of-care reports. The pilot randomized controlled trial recruited adult patients, fluent in English or Spanish, who were seeing a physician or nurse practitioner at the partnering community health centre in Toronto. Randomization into iCCAS or usual care was computer generated, and allocation was concealed in sequentially numbered, opaque envelopes that were opened after consent. The objectives were to examine the interventions9 efficacy in improving mental health discussion (primary) and symptom detection (secondary). Data were collected by exit survey and chart review. <h3>Results:</h3> Of the 1248 patients assessed, 190 were eligible for participation. Of these, 148 were randomly assigned (response rate 78%). The iCCAS (<i>n</i> = 75) and usual care (<i>n</i> = 72) groups were similar in sociodemographics; 98% were immigrants, and 68% were women. Mental health discussion occurred for 58.7% of patients in the iCCAS group and 40.3% in the usual care group (<i>p</i> ≤ 0.05). The effect remained significant while controlling for potential covariates (language, sex, education, employment) in generalized linear mixed model (GLMM; adjusted odds ratio [OR] 2.2; 95% confidence interval [CI] 1.1-4.5). Mental health symptom detection occurred for 38.7% of patients in the iCCAS group and 27.8% in the usual care group (<i>p</i> > 0.05). The effect was not significant beyond potential covariates in GLMM (adjusted OR 1.9; 95% CI 0.9-4.1). <h3>Interpretation:</h3> The studied intervention holds potential for community health centres to improve mental health discussion. Further research with larger samples should examine the impact on detection and enhance generalizability. <b>Trial registration:</b> ClinicalTrials.gov, no: NCT02023957, registered on Dec. 12, 2013.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".