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Record W4390082832 · doi:10.1186/s12961-023-00958-y

A scoping review of the globally available tools for assessing health research partnership outcomes and impacts

2023· review· en· W4390082832 on OpenAlexafffund
Kelly Mrklas, Jamie M. Boyd, Sumair Shergill, Sera Merali, Masood Khan, Cheryl Moser, Lorelli Nowell, Amelia Goertzen, Liam A. Swain, Lisa M. Pfadenhauer, Kathryn M. Sibley, Mathew Vis‐Dunbar, Michael D. Hill, Shelley Raffin‐Bouchal, Marcello Tonelli, Ian D. Graham

Bibliographic record

VenueHealth Research Policy and Systems · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa Public HealthOttawa HospitalUniversity of OttawaHotchkiss Brain InstituteOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaGeorge & Fay Yee Centre for Healthcare InnovationSt. Michael's HospitalAlberta HealthUniversity of ManitobaUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchCumming School of Medicine, University of Calgary
KeywordsGeneral partnershipCINAHLHealth services researchMedicineSystematic reviewMEDLINEThematic analysisPublic healthMedical educationNursingPolitical scienceQualitative researchPsychological interventionSociologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Health research partnership approaches have grown in popularity over the past decade, but the systematic evaluation of their outcomes and impacts has not kept equal pace. Identifying partnership assessment tools and key partnership characteristics is needed to advance partnerships, partnership measurement, and the assessment of their outcomes and impacts through systematic study. OBJECTIVE: To locate and identify globally available tools for assessing the outcomes and impacts of health research partnerships. METHODS: We searched four electronic databases (Ovid MEDLINE, Embase, CINAHL + , PsychINFO) with an a priori strategy from inception to June 2021, without limits. We screened studies independently and in duplicate, keeping only those involving a health research partnership and the development, use and/or assessment of tools to evaluate partnership outcomes and impacts. Reviewer disagreements were resolved by consensus. Study, tool and partnership characteristics, and emerging research questions, gaps and key recommendations were synthesized using descriptive statistics and thematic analysis. RESULTS: We screened 36 027 de-duplicated citations, reviewed 2784 papers in full text, and kept 166 studies and three companion reports. Most studies originated in North America and were published in English after 2015. Most of the 205 tools we identified were questionnaires and surveys targeting researchers, patients and public/community members. While tools were comprehensive and usable, most were designed for single use and lacked validity or reliability evidence. Challenges associated with the interchange and definition of terms (i.e., outcomes, impacts, tool type) were common and may obscure partnership measurement and comparison. Very few of the tools identified in this study overlapped with tools identified by other, similar reviews. Partnership tool development, refinement and evaluation, including tool measurement and optimization, are key areas for future tools-related research. CONCLUSION: This large scoping review identified numerous, single-use tools that require further development and testing to improve their psychometric and scientific qualities. The review also confirmed that the health partnership research domain and its measurement tools are still nascent and actively evolving. Dedicated efforts and resources are required to better understand health research partnerships, partnership optimization and partnership measurement and evaluation using valid, reliable and practical tools that meet partners' needs.

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: Evaluation · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
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)0.229
metaresearch head score (Gemma)0.142
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.371
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2290.142
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0020.006
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.003
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 head0.988
GPT teacher head0.849
Teacher spread0.138 · 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.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review
DomainEvaluation
GenreReview

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

Citations8
Published2023
Admission routes2
Has abstractyes

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