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Record W2906510009 · doi:10.1371/journal.pone.0209826

Engaging stakeholders in the co-development of programs or interventions using Intervention Mapping: A scoping review

2018· review· en· W2906510009 on OpenAlex
Umair Majid, Claire Kim, Albina Cako, Anna R. Gagliardi

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

VenuePLoS ONE · 2018
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsPsychological interventionMEDLINEStakeholderScopusContext (archaeology)MedicineHealth careCochrane LibraryIntervention (counseling)Data extractionFamily medicineMeta-analysisNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Health care innovations tailored to stakeholder context are more readily adopted. This study aimed to describe how Intervention Mapping (IM) was used to design health care innovations and how stakeholders were involved. METHODS: A scoping review was conducted. MEDLINE, EMBASE, Cochrane Library, Scopus and Science Citation Index were searched from 2008 to November 2017. English language studies that used or cited Intervention Mapping were eligible. Screening and data extraction were done in triplicate. Summary statistics were used to describe study characteristics, IM steps employed, and stakeholder involvement. RESULTS: A total of 852 studies were identified, 449 were unique, and 333 were excluded based on title and abstracts, 116 full-text articles were considered and 61 articles representing 60 studies from 13 countries for a variety of clinical issues were included. The number of studies published per year increased since 2008 and doubled in 2016 and 2017. The majority of studies employed multiple research methods (76.7%) and all 6 IM steps (73.3%). Resulting programs/interventions were single (55.4%) or multifaceted (46.4%), and 60.7% were pilot-tested. Programs or interventions were largely educational material or meetings, and were targeted to patients (70.2%), clinicians (14.0%) or both (15.8%). Studies provided few details about current or planned evaluation. Of the 4 (9.3%) studies that reported impact or outcomes, 3 achieved positive improvements in patient or professional behaviour or patient outcomes. Many studies (28.3%) did not involve stakeholders. Those that did (71.7%) often involved a combination of patients, clinicians, and community organizations. However, less than half (48.8%) described how they were engaged. Most often stakeholders were committee members and provide feedback on program or intervention content or format. CONCLUSIONS: It is unclear if use of IM or stakeholder engagement in IM consistently results in effective programs or interventions. Those employing IM should report how stakeholders were involved in each IM step and how involvement influenced program or intervention design. They should also report the details or absence of planned evaluation. Future research should investigate how to optimize stakeholder engagement in IM, and whether use of IM itself or stakeholder engagement in IM are positively associated with effective programs or interventions.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.207
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.981
GPT teacher head0.742
Teacher spread0.239 · 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