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Record W2064150230 · doi:10.1111/phn.12178

A Partnership Development Process Assessment Scale for Public Health Nurses in Japan

2015· article· en· W2064150230 on OpenAlexfundno aff
Yukako Shigematsu, Yoko Hatano, Hitoe Kimura

Bibliographic record

VenuePublic Health Nursing · 2015
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersPublic Health Agency of Canada
KeywordsGeneral partnershipScale (ratio)Public healthPublic health nursingProcess (computing)NursingMedicineEnvironmental healthBusinessGeographyComputer scienceCartography

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to develop and test a Partnership Development Process Assessment (PDPA) scale for content and construct validity and internal consistency reliability. This is needed to document and evaluate community health partnership development processes between public health nurses and community-based organizations in Japan. DESIGN: The study was conducted in three phases. Ten semi-structured interviews were conducted to generate items for a new scale. Thirty items were generated and reviewed by an expert panel for content validity and item refinement. A national postal survey of public health nurses was conducted to determine the scale's internal structure, evaluate its reliability, and explore its construct and criterion validity. MEASURES: Validity and reliability testing of the PDPA scale using a content validity index and analysis of correlations with an existing scale were performed. RESULTS: Twenty-six items were selected and grouped into four factors: activities to share roles to manage community health issues, platform activities to support partnerships, activities to evaluate partnership practices, and activities to share information regarding community health issues. After factor analysis, 23 items were retained. CONCLUSION: The PDPA scale is a valid and reliable instrument for public health nurses to assess partnership development activities.

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)0.020
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.437
GPT teacher head0.560
Teacher spread0.123 · 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 designNot applicable
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

Citations3
Published2015
Admission routes1
Has abstractyes

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