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Population Health Surveillance Practice of Public Health Nurses

2009· article· en· W2171629721 on OpenAlexafffundabout
Donna Meagher‐Stewart, Nancy Edwards, Megan Aston, Linda M. Young

Bibliographic record

VenuePublic Health Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsCapital District Health AuthorityNova Scotia Health AuthorityUniversity of OttawaDalhousie UniversityUniversity of King's College
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsPublic healthPublic health nursingDocumentationPublic health surveillancePopulationPopulation healthNursingMedicineQualitative researchHealth promotionHealth careCommunity healthEnvironmental healthPolitical scienceSociology

Abstract

fetched live from OpenAlex

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.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.414
Teacher spread0.343 · 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 designOther design
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

Citations8
Published2009
Admission routes3
Has abstractyes

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