Health Inequities and Moral Distress Among Community Health Nurses During the COVID-19 Pandemic
Bibliographic record
Abstract
The core values of community health nursing practice are rooted in the social determinants of health, health equity and social justice. Throughout the COVID-19 pandemic, community health nurses (CHNs) witnessed first-hand the impact on individuals in situations of marginalization. This research inquiry explored how health inequities among client populations contributed to experiences of MD among CHNs in Canada during the pandemic. A total of 245 CHNs from across Canada participated in an online survey. Participants reported that during the pandemic individuals living in situations of marginalization were disproportionately impacted. CHNs were unable to provide the necessary health promotion interventions and experienced high levels of moral distress. The negative impact of the pandemic on individuals living in situations of marginalization illuminated the intersecting social and structural inequities that drive negative health outcomes and emphasized the need to adopt an equity focus for current and future pandemic planning, response, and recovery.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".