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Record W4321458891 · doi:10.1177/08445621221143019

A Multiphase Mixed Methods Study on the Integration of a Population Health Approach in Sexual Health Programs and Services in Ontario Public Health Units

2023· article· en· W4321458891 on OpenAlexaffvenueabout
Linda Frost, Ruta Valaitis, Susan M. Jack, Michelle Butt, Noori Akhtar‐Danesh

Bibliographic record

VenueCanadian Journal of Nursing Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPublic healthReproductive healthPopulationQualitative researchPopulation healthHealth policyQualitative propertyHealth promotionEnvironmental healthPsychologyMedicineNursingSociologyComputer science

Abstract

fetched live from OpenAlex

PURPOSE: This study investigated the extent of and factors influencing implementation of a population health approach within sexual health programming in public health. METHOD: This sequential multi-phase mixed methods study combined findings from a quantitative survey assessing the extent that a population health approach was implemented in sexual health programs in Ontario public health units and qualitative interviews with sexual health managers and/or supervisors. Interviews explored factors influencing implementation and were analyzed using directed content analysis. RESULTS: Staff from fifteen of 34 public health units completed surveys and ten interviews were completed with sexual health managers/supervisors. From the 8 Population Health Key Elements Template, 6 elements were moderately implemented and 2 had low implementation. Qualitative findings focused on enablers and barriers to implementing a population health approach in sexual health programs and services and explained most of the quantitative results. However, some of the quantitative findings were not explained by qualitative data (e.g., low implementation of using the principles of social justice). CONCLUSION: Qualitative findings revealed factors influencing the implementation of a population health approach. A lack of resources available to health units, differing priorities between health units and community stakeholders, and access to evidence around population-level interventions influenced implementation.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.926
GPT teacher head0.740
Teacher spread0.187 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Other 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

Citations1
Published2023
Admission routes3
Has abstractyes

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