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Record W1542058915

Partnerships in public health: lessons from knowledge translation and program planning.

2012· article· en· W1542058915 on OpenAlexaffabout
Shannon L. Sibbald, Anita Kothari, Debbie Laliberté Rudman, Maureen Dobbins, Michael J. Rouse, Nancy Edwards, Dana Gore

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSimon Fraser UniversityUniversity of OttawaMcMaster UniversityWestern University
Fundersnot available
KeywordsGeneral partnershipPublic healthFocus groupPublic relationsExperiential knowledgeKnowledge translationExperiential learningQualitative researchMedical educationProcess (computing)BusinessPolitical scienceMedicineNursingPsychologyKnowledge managementSociologyPedagogyMarketing
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to better understand how partnerships are initiated, maintained, and sustained in public health practice. A qualitative design was employed to conduct individual interviews and focus groups. The participants included practitioners from 6 purposively selected public health units in the Canadian province of Ontario that developed partnerships in program planning. It was found that partnerships play an essential role in program planning but that minimal information is available regarding the partnership process. Most partnerships are formed on an ad hoc basis, with little formalization. Public health professionals rely on their experiential knowledge when seeking out and working with partners.These findings can serve to inform future public health planning and strengthen the formation and maintenance of partnerships in public health and other sectors. Understanding how partnerships are initiated, maintained, and sustained is an important first step in supporting the use of research to advance collaborative public health efforts.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.930
GPT teacher head0.679
Teacher spread0.251 · 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.

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

Citations15
Published2012
Admission routes2
Has abstractyes

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