Population Health Surveillance Practice of Public Health Nurses
Bibliographic record
Abstract
OBJECTIVE: To report the population health surveillance functions of public health nurses and to describe factors that impede these functions. DESIGN AND SAMPLE: An interpretive qualitative study was conducted in Public Health Service areas in Eastern Canada. Participants were public health nurses (n=55) with an average of 14.5 years of pertinent work experience. MEASURES: Semistructured face-to-face, telephone interviews, and focus groups were conducted, transcribed, coded, and analyzed. RESULTS: The nurses in this study used ecosocial population health surveillance functions that included multilevel societal influences on health. Extensive interprofessional and intersectoral networks were foundational to their surveillance work, allowing them to monitor what was occurring in the community and transfer this knowledge into various systems to contribute toward improved health outcomes. However, the nurses did not acknowledge the significance of their population health surveillance work, and documentation structures did not support these surveillance functions. CONCLUSION: New surveillance methods and documentation structures that reflect an ecosocial surveillance approach are needed that are more consistent with public health nurses' population-focused practice.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".