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Record W4243729416 · doi:10.1007/s13142-016-0399-3

Mapping training needs for dissemination and implementation research: lessons from a synthesis of existing D&I research training programs

2016· review· en· W4243729416 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTranslational Behavioral Medicine · 2016
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSt. Michael's Hospital
FundersNational Center for Advancing Translational SciencesOffice of Behavioral and Social Sciences ResearchNational Center for Complementary and Alternative MedicineNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthUniversity of California, San FranciscoUniversity of California, San DiegoWeill Cornell Medical CollegeQuality Enhancement Research InitiativeNational Cancer InstituteUniversity of TorontoAcademyHealthInstitute of Clinical and Translational SciencesUniversity of WashingtonUniversity of North Carolina at Chapel HillOhio State UniversityNational Institutes of HealthWashington University in St. LouisNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins University
KeywordsCourseworkTraining (meteorology)Medical educationGrant writingMentorshipNeeds assessmentComputer sciencePsychologyMedicineLibrary sciencePolitical science

Abstract

fetched live from OpenAlex

With recent growth in the field of dissemination and implementation (D&I) research, multiple training programs have been developed to build capacity, including summer training institutes, graduate courses, degree programs, workshops, and conferences. While opportunities for D&I research training have expanded, course organizers acknowledge that available slots are insufficient to meet demand within the scientific and practitioner community. In addition, individual programs have struggled to best fit various needs of trainees, sometimes splitting coursework between specific D&I content and more introductory grant writing material. This article, stemming from a 2013 NIH workshop, reviews experiences across multiple training programs to align training needs, career stage and role, and availability of programs. We briefly review D&I needs and opportunities by career stage and role, discuss variations among existing training programs in format, mentoring relationships, and other characteristics, identify challenges of mapping needs of trainees to programs, and present recommendations for future D&I research training.

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.

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
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
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)0.022
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.979
GPT teacher head0.818
Teacher spread0.161 · 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