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Record W2128473868 · doi:10.1186/s13012-015-0304-3

Developing educational competencies for dissemination and implementation research training programs: an exploratory analysis using card sorts

2015· article· en· W2128473868 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

VenueImplementation Science · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster University
FundersNational Center for Advancing Translational SciencesNational Cancer InstituteNational Institutes of HealthInstitute of Clinical and Translational SciencesWashington University in St. Louis
KeywordsCard sortingMedical educationCurriculumMedicineCategorizationKnowledge translationExploratory researchKnowledge managementPsychologyComputer sciencePedagogyManagement

Abstract

fetched live from OpenAlex

BACKGROUND: With demand increasing for dissemination and implementation (D&I) training programs in the USA and other countries, more structured, competency-based, and tested curricula are needed to guide training programs. There are many benefits to the use of competencies in practice-based education such as the establishment of rigorous standards as well as providing an additional metrics for development and growth. As the first aim of a D&I training grant, an exploratory study was conducted to establish a new set of D&I competencies to guide training in D&I research. METHODS: Based upon existing D&I training literature, the leadership team compiled an initial list of competencies. The research team then engaged 16 additional colleagues in the area of D&I science to provide suggestions to the initial list. The competency list was then additionally narrowed to 43 unique competencies following feedback elicited from these D&I researchers. Three hundred additional D&I researchers were then invited via email to complete a card sort in which the list of competencies were sorted into three categories of experience levels. Participants had previous first-hand experience with D&I or knowledge translation training programs in the past. Participants reported their self-identified D&I expertise level as well as the country in which their home institution is located. A mean score was calculated for each competency based on their experience level categorization. From these mean scores, beginner-, intermediate-, and advanced-level tertiles were created for the competencies. RESULTS: The card sort request achieved a 41 % response rate (n = 124). The list of 43 competencies was organized into four broad domains and sorted based on their experience level score. Eleven competencies were classified into the "Beginner" category, 27 into "Intermediate," and 5 into "Advanced." CONCLUSIONS: Education and training developers can use this competency list to formalize future trainings in D&I research, create more evidence-informed curricula, and enable overall capacity building and accompanying metrics in the field of D&I training and research.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0040.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.936
GPT teacher head0.802
Teacher spread0.134 · 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