Public health nursing practice with ‘high priority’ families: the significance of contextualizing ‘risk’
Bibliographic record
Abstract
BROWNE AJ, HARTRICK DOANE G, REIMER J, MacLEOD MLP and McLELLAN E. Nursing Inquiry 2010; 17: 27–38 Public health nursing practice with ‘high priority’ families: the significance of contextualizing ‘risk’ Public health nurses (PHNs) play a vital role in supporting families at risk; few studies, however, have focused on how PHNs actually work with families to provide support, build trust, and use their clinical judgment to make decisions in complex, at‐risk situations. In this study, we report on findings from research that illustrate how PHNs use relational approaches in their work with ‘high priority’ families. Drawing on data collected from interviews and focus groups with 32 PHNs, we discuss three central features inherent to working relationally with families at risk: (i) contextualizing the complexities of families’ lives; (ii) responding to shifting contexts of risk and capacity; and (iii) working relationally with families under surveillance. These findings show that the ability to recognize risk and capacity as intersecting aspects of families’ lives, and to practice from a stance that recognizes risk as contextualized is foundational to effective working relationships with high‐priority families.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| 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".