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Record W4312105028 · doi:10.1093/geroni/igac059.1237

DEVELOPMENTAL ADVANCES OF THE PARIHS FRAMEWORK OVER THE LAST DECADE: A CRITICAL INTERPRETIVE SYNTHESIS

2022· article· en· W4312105028 on OpenAlexaff
Whitney Berta, Yinfei Duan, Alba Iaconi, Jing Wang, Janelle Perez, Yuting Song, Stephanie Chamberlain, Carole A. Estabrooks

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsFacilitationTypologyContext (archaeology)Core (optical fiber)Knowledge managementPsychologyEngineering ethicsMedicineSociologyComputer scienceEngineeringNeuroscience

Abstract

fetched live from OpenAlex

Abstract The original Promoting Action on Research Implementation in Health Services (PARiHS) and i-PARiHS frameworks contend that the successful implementation of evidence-based practices is a function of the core elements evidence, context, facilitation, and the capacity of intended recipients to apply research to practice. While applied widely, a number of theoretical and practical challenges associated with the framework’s application have been identified. Our critical interpretive synthesis examines how the last decade of research has advanced understanding of the conceptualizations of, relationships between, and dynamics amongst, PARiHS core elements. We find that work over the past decade affords more nuanced conceptualizations of context and facilitation; reveals myriad conceptualizations of implementation success, suggesting the need for a typology; demonstrates contradictory effects of context on facilitation that warrants more study; leads us to question the contextual primacy of leadership; and generally under-examines the interactions and dynamics amongst PARiHS core elements and their sub-elements.

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)0.199
metaresearch head score (Gemma)0.160
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.199
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1990.160
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0200.014
Science and technology studies0.0080.046
Scholarly communication0.0200.022
Open science0.0040.014
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0050.000

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 head0.200
GPT teacher head0.592
Teacher spread0.392 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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