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Record W2323686291 · doi:10.1007/s13142-016-0411-y

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

2016· erratum· en· W2323686291 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.

Bibliographic record

VenueTranslational Behavioral Medicine · 2016
Typeerratum
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsTraining (meteorology)Medical educationHealth psychologyComputer sciencePsychologyMedicinePublic healthNursingGeography

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: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement 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)0.020
metaresearch head score (Gemma)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.942
GPT teacher head0.764
Teacher spread0.178 · 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