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Record W2898875739 · doi:10.9778/cmajo.20180143

Assessment of scalability of evidence-based innovations in community-based primary health care: a cross-sectional study

2018· article· en· W2898875739 on OpenAlexafffundvenueabout
Ali Ben Charif, Kasra Hassani, Sabrina T. Wong, Hervé Tchala Vignon Zomahoun, Martin Fortin, Adriana Freitas, Alan Katz, Claire Kendall, Clare Liddy, Kathryn Nicholson, Bojana Petrovic, Jenny Ploeg, France Légaré

Bibliographic record

VenueCMAJ Open · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité LavalUniversity of OttawaWestern University
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionDimension (graph theory)ScalabilityStandard deviationCross-sectional studyHealth careMedicineKnowledge managementComputer scienceNursingStatisticsMathematics

Abstract

fetched live from OpenAlex

Background: In 2013, the Canadian Institutes of Health Research funded 12 community-based primary health care research teams to develop evidence-based innovations. We aimed to explore the scalability of these innovations. Methods: In this cross-sectional study, we invited the 12 teams to rate their evidence-based innovations for scalability. Based on a systematic review, we developed a self-administered questionnaire with 16 scalability assessment criteria grouped into 5 dimensions (theory, impact, coverage, setting and cost). Teams completed a questionnaire for each of their innovations. We analyzed the data using simple frequency counts and hierarchical cluster analysis. We calculated the mean number and standard deviation (SD) of innovations that met criteria within each dimension that included more than 1 criterion. The analysis unit was the innovation. Results: The 11 responding teams evaluated 33 evidence-based innovations (median 3, range 1–8 per team). The innovations focused on access to care and chronic disease prevention and management, and varied from health interventions to methodological innovations. Most of the innovations were health interventions (n = 21), followed by analytical methods (n = 4), conceptual frameworks (n = 4), measures (n = 3) and strategies to build research capacity (n = 1). Most (29) met criteria in the theory dimension, followed by impact (mean 22.3 [SD 5.6] innovations per dimension), setting (mean 21.7 [SD 8.5]), cost (mean 17.5 [SD 2.1]) and coverage (mean 14.0 [SD 4.1]). On average, the innovations met 10 of the 16 criteria. Adoption was the least assessed criterion (n = 9). Most (20) of the innovations were highly ranked for scalability. Interpretation: Scalability varied among innovations, which suggests that readiness for scale up was suboptimal for some innovations. Coverage remained largely unaddressed; further investigation of this critical dimension is necessary.

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)0.141
metaresearch head score (Gemma)0.224
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.748

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1410.224
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0070.007
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.778
GPT teacher head0.727
Teacher spread0.050 · 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.

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".

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Citations38
Published2018
Admission routes4
Has abstractyes

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