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Record W2391110362 · doi:10.9778/cmajo.20150045

Health services and policy research in the first decadeat the Canadian Institutes of Health Research

2016· article· en· W2391110362 on OpenAlexafffundvenueabout
Robyn Tamblyn, Meghan McMahon, Nathalie Girard, Elise Drake, Jessica Nadigel, Kim Gaudreau

Bibliographic record

VenueCMAJ Open · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsInstitute of Health Services and Policy ResearchCanadian Institutes of Health Research
FundersCanadian Institutes of Health ResearchMedical Research CouncilAustralian Government
KeywordsCritical mass (sociodynamics)Health careGrant fundingLogistic regressionConfidence intervalPolitical scienceHealth services researchMedicineMedical educationSociologyPublic administrationSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Health services and policy research is the innovation engine of a health care system. In 2000, the Canadian Institutes of Health Research (CIHR) was formed to foster the growth of all sciences that could improve health care. We evaluated trends in health services and policy research funding, in addition to determinants of funding success. METHODS: All applications submitted to CIHR strategic and open operating grant competitions between 2001 and 2011 were included in our analysis. Age, sex, size of research team, critical mass, season, year and research discipline were retrieved from application information. A cohort of 4725 applicants successfully funded between 2001 and 2005 were followed for 5 years to evaluate predictors of continuous funding. Multivariate generalized estimating equation logistic regression was used to estimate predictors of funding success and sustained funding. RESULTS: Between 2001 and 2011, 80 163 applications were submitted to open and strategic grant competitions. Over time, grant applications increased from 327 to 1137 per year, and annual funding increased from $12.6 to $48.0 million. Grant applications from young male researchers were more likely to be funded than those from female researchers (odds ratio [OR] 1.40, 95% confidence interval [CI] 1.01-1.95), as were applications from larger research teams and institutions with a large critical mass. Only 24.0% of scientists whose first funded grant was in health services and policy research had sustained 5-year funding, compared with 52.8% of biomedical scientists (OR 0.34, 95% CI 0.24-0.49). INTERPRETATION: The CIHR has successfully increased the amount of health services and policy research in Canada. To enhance conditions for success, researchers should be encouraged to work in teams, request longer duration grants, resubmit unsuccessful applications and affiliate themselves with institutions with a greater critical mass.

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)0.045
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.017
Science and technology studies0.0060.005
Scholarly communication0.0100.002
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.546
GPT teacher head0.609
Teacher spread0.064 · 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 designObservational
DomainEvaluation
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

Citations28
Published2016
Admission routes4
Has abstractyes

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