Psychosocial screening, in-patient care, and disposition planning: Clinicians’ perspectives
Bibliographic record
Abstract
ObjectiveOur objective was to explore clinicians' views on the MyHEARTSMAP screening report; whether this report has impacted their patient care, and if so, how. MyHEARTSMAP is a psychosocial self-screening tool for youth to identify mental health concerns.MethodsWe conducted a cross-sectional study as a sub-study of the MyHEARTSMAP In-Patient randomized control trial. Eligible clinicians (nurses and physicians who have cared for patients in one of our partnered specialties and have seen a MyHEARTSMAP report in their patients' charts) provided their perceptions of the screening report through a survey.ResultsSixty-five clinicians were enrolled; 60 (92.3%; 95% CI 85.8-98.8%) believe psychosocial screening is beneficial, with many finding it helpful for building rapport with patients/families and providing additional mental health information. Thirty-seven clinicians (56.9%; 95% CI 44.9-69%) had previously read or used the MyHEARTSMAP report, and 31 (83.8%; 95% CI 71.9-95.7%) of these clinicians found the report helpful. Clinicians specifically found the report helpful for communicating with the patient, and guiding patient-centered care.ConclusionClinicians' perceptions towards the MyHEARTSMAP report were positive amongst those who had previously encountered it. While clinicians believe psychosocial screening is beneficial, exploring options for better accessibility to the screening results is necessary to increase utilization.
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 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 | 0.026 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".