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Record W4313561620 · doi:10.1186/s12961-022-00937-9

Tools for assessing health research partnership outcomes and impacts: a systematic review

2023· review· en· W4313561620 on OpenAlexafffund
Kelly Mrklas, Jeffrey Boyd, Sumair Shergill, Sera Merali, Masood Khan, Lorelli Nowell, Amelia Goertzen, Lisa M. Pfadenhauer, Kevin Paúl, Kathryn M. Sibley, Liam A. Swain, 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 CalgaryUniversity of Manitoba
KeywordsHealth services researchPublic healthGeneral partnershipHealth administrationHealth economicsMedicineHealth policySocial policyEnvironmental healthNursingPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify and assess the globally available valid, reliable and acceptable tools for assessing health research partnership outcomes and impacts. METHODS: We searched Ovid MEDLINE, Embase, CINAHL Plus and PsycINFO from origin to 2 June 2021, without limits, using an a priori strategy and registered protocol. We screened citations independently and in duplicate, resolving discrepancies by consensus and retaining studies involving health research partnerships, the development, use and/or assessment of tools to evaluate partnership outcomes and impacts, and reporting empirical psychometric evidence. Study, tool, psychometric and pragmatic characteristics were abstracted using a hybrid approach, then synthesized using descriptive statistics and thematic analysis. Study quality was assessed using the quality of survey studies in psychology (Q-SSP) checklist. RESULTS: From 56 123 total citations, we screened 36 027 citations, assessed 2784 full-text papers, abstracted data from 48 studies and one companion report, and identified 58 tools. Most tools comprised surveys, questionnaires and scales. Studies used cross-sectional or mixed-method/embedded survey designs and employed quantitative and mixed methods. Both studies and tools were conceptually well grounded, focusing mainly on outcomes, then process, and less frequently on impact measurement. Multiple forms of empirical validity and reliability evidence was present for most tools; however, psychometric characteristics were inconsistently assessed and reported. We identified a subset of studies (22) and accompanying tools distinguished by their empirical psychometric, pragmatic and study quality characteristics. While our review demonstrated psychometric and pragmatic improvements over previous reviews, challenges related to health research partnership assessment and the nascency of partnership science persist. CONCLUSION: This systematic review identified multiple tools demonstrating empirical psychometric evidence, pragmatic strength and moderate study quality. Increased attention to psychometric and pragmatic requirements in tool development, testing and reporting is key to advancing health research partnership assessment and partnership science. PROSPERO CRD42021137932.

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

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)0.307
metaresearch head score (Gemma)0.228
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3070.228
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.000
Bibliometrics0.0040.006
Science and technology studies0.0090.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.001

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 head0.990
GPT teacher head0.865
Teacher spread0.126 · 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

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
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

Citations36
Published2023
Admission routes2
Has abstractyes

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