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Record W2558281428 · doi:10.5864/d2016-024

Can focus groups be a tool for change? Introducing health equity to environmental public health practice

2016· article· en· W2558281428 on OpenAlexaffvenue
Karen Rideout, Dianne Oickle, Connie Clement

Bibliographic record

VenueEnvironmental Health Review · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsBC Centre for Disease Control
Fundersnot available
KeywordsFocus groupPublic healthPublic relationsQualitative researchEquity (law)PsychologyFocus (optics)Knowledge managementPolitical scienceMedicineSociologyNursingComputer scienceSocial science

Abstract

fetched live from OpenAlex

This qualitative investigation was undertaken to explore the value of focus group participation to introduce new concepts into practice within public health. Seven public health inspectors who participated in an earlier focus group study responded to follow-up questions designed to assess whether their participation in the original focus group sessions lead to changes in their thinking or practice. Findings suggest that focus group participation can provide an opportunity to start conversations about new concepts, highlight ways to put thoughts into action, validate how current practice supports broader goals, and identify gaps and next steps. Although an important tool for change, systematic change requires additional support at the organizational level to achieve full implementation. Further research into the use of focus groups as a tool for reflective practice is recommended.

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 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)0.262
metaresearch head score (Gemma)0.292
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.262
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2620.292
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.022
Scholarly communication0.0080.026
Open science0.0040.011
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.615
GPT teacher head0.640
Teacher spread0.025 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2016
Admission routes2
Has abstractyes

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