Perceptions of Educational Needs in an Era of Shifting Mental Health Care to Primary Care: Exploratory Pilot Study
Bibliographic record
Abstract
BACKGROUND: There is an unmet need for mental health care in Canada. Primary care providers such as general practitioners and family physicians are the essential part of mental health care services; however, mental health is often underestimated and underprioritized by family physicians. It is currently not known what is required to increase care providers' willingness, comfort, and skills to adequately provide care to patients who present with mental health issues. OBJECTIVE: The aim of this study was to understand the need of caregivers (family members overseeing care of an individual with a mental health diagnosis) and family physicians regarding the care and medical management of individuals with mental health conditions. METHODS: A needs assessment was designed to understand the educational needs of caregivers and family physicians regarding the provision of mental health care, specifically to seek advice on the format and delivery mode for an educational curriculum to be accessed by both stakeholder groups. Exploratory qualitative interviews were conducted, and data were collected and analyzed iteratively until thematic saturation was achieved. RESULTS: Caregivers of individuals with mental health conditions (n=24) and family physicians (n=10) were interviewed. Both the caregivers and the family physicians expressed dissatisfaction with the status quo regarding the provision of mental health care at the family physician's office. They stated that there was a need for more educational materials as well as additional support. The caregivers expressed a general lack of confidence in family physicians to manage their son's or daughter's mental health condition, while family physicians sought more networking opportunities to improve and facilitate the provision of mental health care. CONCLUSIONS: Robust qualitative studies are necessary to identify the educational and medical management needs of caregivers and family physicians. Understanding each other's perspectives is an essential first step to collaboratively designing, implementing, and subsequently evaluating community-based mental health care. Fortunately, there are initiatives underway to address these need areas (eg, websites such as the eMentalHealth, as well as the mentorship and collaborative care network), and information from this study can help inform the gaps in those existing initiatives.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".