MétaCan
Menu
Back to cohort

Public health nursing practice with ‘high priority’ families: the significance of contextualizing ‘risk’

2010· article· en· W2078107932 on OpenAlexafffund
Annette J. Browne, Gweneth Hartrick Doane, Joanne Chekryn Reimer, Martha MacLeod, Edna McLellan

Bibliographic record

VenueNursing Inquiry · 2010
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Northern British ColumbiaUniversity of VictoriaUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersCanadian Institutes of Health ResearchHealth Research Board
KeywordsPublic healthNursing practiceNursingPublic health nursingMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.263
GPT teacher head0.483
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueNursing InquirySame topicFood Security and Health in Diverse PopulationsFrench-language works237,207