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Record W4399301157 · doi:10.1186/s13643-024-02563-8

Co-production of a systematic review on decision coaching: a mixed methods case study within a review

2024· review· en· W4399301157 on OpenAlexafffund
Janet Jull, Maureen Smith, Meg Carley, Dawn Stacey, Ian D. Graham, Laura Boland, Sandra Dunn, Andrew Dwyer, Jeanette Finderup, Jürgen Kasper, Simone Kienlin, Sascha Köpke, France Légaré, Krystina B. Lewis, Anne Christin Rahn, Claudia Rutherford, Junqiang Zhao

Bibliographic record

VenueSystematic Reviews · 2024
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of OttawaCochraneQueensway-Carleton HospitalOttawa HospitalQueen's University
FundersCanadian Institutes of Health ResearchUniversitätsklinikum KölnFaculty of Medicine and Health, University of SydneyNovo Nordisk FondenUniversität zu LübeckUniversity of TorontoUniversity of SydneyUniversity of OttawaUniversité Laval
KeywordsKnowledge translationCoachingMedicineSystematic reviewMedical educationKnowledge managementMEDLINEPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Co-production is a collaborative approach to prepare, plan, conduct, and apply research with those who will use or be impacted by research (knowledge users). Our team of knowledge users and researchers sought to conduct and evaluate co-production of a systematic review on decision coaching. METHODS: We conducted a mixed-methods case study within a review to describe team co-production of a systematic review. We used the Collaborative Research Framework to support an integrated knowledge translation approach to guide a team through the steps in co-production of a systematic review. The team agreed to conduct self-study as a study within a review to learn from belonging to a co-production research team. A core group that includes a patient partner developed and conducted the study within a review. Data sources were surveys and documents. The study coordinator administered surveys to determine participant preferred and actual levels of engagement, experiences, and perceptions. We included frequency counts, content, and document analysis. RESULTS: We describe co-production of a systematic review. Of 17 team members, 14 (82%) agreed to study participation and of those 12 (86%) provided data pre- and post-systematic review. Most participants identified as women (n = 9, 75.0%), researchers (n = 7, 58%), trainees (n = 4, 33%), and/or clinicians (n = 2, 17%) with two patient/caregiver partners (17%). The team self-organized study governance with an executive and Steering Committee and agreed on research co-production actions and strategies. Satisfaction for engagement in the 11 systematic review steps ranged from 75 to 92%, with one participant who did not respond to any of the questions (8%) for all. Participants reported positive experiences with team communication processes (n = 12, 100%), collaboration (n = 12, 100%), and negotiation (n = 10-12, 83-100%). Participants perceived the systematic review as co-produced (n = 12, 100%) with collaborative (n = 8, 67%) and engagement activities to characterize co-production (n = 8, 67%). Participants indicated that they would not change the co-production approach (n = 8, 66%). Five participants (42%) reported team logistics challenges and four (33%) were unaware of challenges. CONCLUSIONS: Our results indicate that it is feasible to use an integrated knowledge translation approach to conduct a systematic review. We demonstrate the importance of a relational approach to research co-production, and that it is essential to plan and actively support team engagement in the research lifecycle.

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

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
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
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)0.128
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1280.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0330.003
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.006

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 head0.648
GPT teacher head0.637
Teacher spread0.011 · 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

Labeled directly by 2 models reading the full record.

Study designQualitative
DomainMethods
GenreReview · Empirical

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

Citations3
Published2024
Admission routes2
Has abstractyes

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