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Record W3016456460 · doi:10.1186/s12961-020-00548-2

Stakeholders’ engagement in co-producing policy-relevant knowledge to facilitate employment for persons with developmental disabilities

2020· article· en· W3016456460 on OpenAlexafffund
Akram Khayatzadeh‐Mahani, Krystle Wittevrongel, Lisa Petermann, Ian D. Graham, Jennifer Zwicker

Bibliographic record

VenueHealth Research Policy and Systems · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa HospitalUniversity of OttawaLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersSinneave Family Foundation
KeywordsStakeholder engagementKnowledge translationStakeholderPublic relationsSocial policyHealth services researchBusinessHealth administrationFace (sociological concept)Public engagementHealth policyKnowledge managementPsychologyPublic healthPolitical scienceNursingSociologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Persons with developmental disabilities (PWDD) face a number of individual, environmental and societal barriers when seeking employment. Integrated knowledge translation (IKT) involves ongoing and dynamic interactions between researchers and stakeholders for the purpose of engaging in mutually beneficial research to address these types of multi-faceted barriers. There is a knowledge gap in the IKT literature on effective stakeholder engagement strategies outside of the dissemination stage to inform policy. In this paper, we report on a number of engagement strategies employed over a 2-year period to engage a wide range of stakeholders in different stages of an IKT project that aimed to investigate the 'wicked' problem of employment for PWDD. METHOD: Our engagement plan included multiple linked strategies and was designed to ensure the meaningful engagement of, and knowledge co-production with, stakeholders. We held two participatory consensus-building stakeholder policy dialogue events to co-produce knowledge utilising the nominal group technique and the modified Delphi technique. A total of 31 and 49 stakeholders engaged in the first and second events, respectively, from six key stakeholder groups. Focused engagement strategies were employed to build on the stakeholder dialogues for knowledge mobilisation and included a focus group attended only by PWDD, a stakeholder workshop attended only by policy/decision-makers, a webinar attended by human resources professionals and employers, and a current affairs panel attended by the general public. RESULTS: Our findings suggest that the level of engagement for each stakeholder group varies depending on the goal and need of the project. Our stakeholder dialogue findings highlight the inherent challenges in co-framing and knowledge co-production through the meaningful engagement of multiple stakeholders who hold different ideas and interests. Focused outreach is needed to foster relationships and trust for meaningful engagement. CONCLUSIONS: In addition to providing guidance on how to implement adaptable meaningful engagement strategies, these findings contribute to discussions on how IKT projects are planned and funded. More studies to explore effective mechanisms for engaging a wide range of stakeholders in IKT research are needed. More evidence of successful engagement strategies employed by researchers to achieve meaningful knowledge co-production is also key to advancing the discipline.

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)0.020
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.956
GPT teacher head0.704
Teacher spread0.252 · 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; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations12
Published2020
Admission routes2
Has abstractyes

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