Supporting the Mental Health of Primary Care Nurses and Staff through the Pandemic and Beyond
Bibliographic record
Abstract
As a clinical nurse specialist, I provide leadership and strategy for our primary care program where I lead clinical initiatives and develop practice tools and guidelines across our clinics. My portfolio encompasses five clinics, one perinatal program, an opioid agonist therapy (OAT) clinic and an intensive case management team, and in the past year I supported several teams that focus on COVID-19 testing and isolation support. Our clinics specialize in serving people who experience significant economic and social marginalization and those who are not well served by traditional health services. Our nurses, in particular, juggle many roles: providing both outreach- and clinic-based care and supporting our injectable OAT program, youth clinic and our transgender specialty care program. Our work has become increasingly complex as our clients navigate survival with competing syndemics - the opioid crisis, COVID-19, a Shigella outbreak and an ongoing housing crisis - among the many significant structural factors that impact our clients' health.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".