The Perceived Educational Needs of Primary Healthcare Nurses Providing Mental Healthcare
Bibliographic record
Abstract
Mental healthcare in Canada is a pressing concern, with primary healthcare (PHC) serving as a key setting for addressing these needs. PHC nurses, often the first point of contact for patients, play a critical role in delivering community-based mental healthcare. However, their specific role and educational needs remain understudied. Using Interpretive Description, this study explored the perceived education needs of five nurses working in a Calgary Primary Care Network (PCN). Interviews were transcribed and thematically analyzed using NVivo, revealing four key themes: Mental Health Experience, Role of PHC Nurse, Impact of Mental Health Education, and Organizational Facilitators and Barriers. Findings showed that participants had limited formal mental health education prior to working at the PCN and preferred interactive learning (e.g., shadowing, mentorship, case studies) over online modules. Mental health education improved their confidence and preparedness in delivering care. Participants also highlighted their dual role in managing both chronic diseases and mental health concerns, though their scope of practice varied across Calgary PCNs. Key implications include tailoring education to PHC nurses’ roles, emphasizing interactive education, integrating mental health training into nursing curricula, and clarifying PHC nurses’ scope of practice system wide. Future research should evaluate mental health education programs and compare nurse preparedness across provinces to inform national strategies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".