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Record W2799807658 · doi:10.1155/2018/1519402

The Interplay between Participatory Health Research and Implementation Research: Canadian Research Funding Perspectives

2018· review· en· W2799807658 on OpenAlexaffabout
Erica Di Ruggiero, Nancy Edwards

Bibliographic record

VenueBioMed Research International · 2018
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionParticipatory action researchCitizen journalismPublic relationsCommunity-based participatory researchScale (ratio)Implementation researchUnintended consequencesIntervention (counseling)Intersection (aeronautics)Political scienceSociologyMedicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this paper is to investigate what participatory health research (PHR) can offer implementation research (IR) and vice versa and discuss what health research funders can do to foster the intersection of both fields. METHODS: We contrast points of divergence and convergence between IR and PHR. We reflect on whether community engagement is necessary and on the unintended consequences of participation in IR. We describe how a research funder can incentivize PHR in IR. RESULTS: Participation is encouraged in IR but the nuances of who is involved merit greater attention in IR. PHR and IR differ in emphasis placed on the scale-up of the intervention. However, they share a common interest in generating real-world contextually relevant evidence. CONCLUSIONS: We need to assess whether and how funding practices influence researchers in how they undertake PHR. Researchers need to better account for participatory approaches to ensure that any potentially harmful consequences are described (and better understood so they can be mitigated in the future) and elucidate the ways in which these processes do or do not enable implementation and scale-up of interventions in IR.

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
gemmaMetaresearch
Domain: Incentives · Genre: Review
About the Canadian research system: yes · About a Canadian topic: yes
Not applicablelow
gptMetaresearch
Domain: Incentives · Genre: Review
About the Canadian research system: yes · About a Canadian topic: yes
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.312
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.281
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3120.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0120.008
Science and technology studies0.0250.008
Scholarly communication0.0010.001
Open science0.0040.003
Research integrity0.0010.012
Insufficient payload (model declined to judge)0.0020.004

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 head0.979
GPT teacher head0.864
Teacher spread0.115 · 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.

Metaresearch

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

Study designNot applicable · Other design
DomainIncentives
GenreReview

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

Citations17
Published2018
Admission routes2
Has abstractyes

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