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Record W2153258882 · doi:10.1186/1471-2458-13-428

A framework for stakeholder identification in concept mapping and health research: a novel process and its application to older adult mobility and the built environment

2013· article· en· W2153258882 on OpenAlexafffund
Claire Schiller, Meghan Winters, Heather Hanson, Maureen C. Ashe

Bibliographic record

VenueBMC Public Health · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityBritish Columbia Centre of Excellence for Women's HealthVancouver Coastal Health
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsStakeholderStakeholder analysisIdentification (biology)Knowledge managementProcess (computing)Stakeholder engagementMedicinePublic relationsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Stakeholders, as originally defined in theory, are groups or individual who can affect or are affected by an issue. Stakeholders are an important source of information in health research, providing critical perspectives and new insights on the complex determinants of health. The intersection of built and social environments with older adult mobility is an area of research that is fundamentally interdisciplinary and would benefit from a better understanding of stakeholder perspectives. Although a rich body of literature surrounds stakeholder theory, a systematic process for identifying health stakeholders in practice does not exist. This paper presents a framework of stakeholders related to older adult mobility and the built environment, and further outlines a process for systematically identifying stakeholders that can be applied in other health contexts, with a particular emphasis on concept mapping research. METHODS: Informed by gaps in the relevant literature we developed a framework for identifying and categorizing health stakeholders. The framework was created through a novel iterative process of stakeholder identification and categorization. The development entailed a literature search to identify stakeholder categories, representation of identified stakeholders in a visual chart, and correspondence with expert informants to obtain practice-based insight. RESULTS: The three-step, iterative creation process progressed from identifying stakeholder categories, to identifying specific stakeholder groups and soliciting feedback from expert informants. The result was a stakeholder framework comprised of seven categories with detailed sub-groups. The main categories of stakeholders were, (1) the Public, (2) Policy makers and governments, (3) Research community, (4) Practitioners and professionals, (5) Health and social service providers, (6) Civil society organizations, and (7) Private business. CONCLUSIONS: Stakeholders related to older adult mobility and the built environment span many disciplines and realms of practice. Researchers studying this issue may use the detailed stakeholder framework process we present to identify participants for future projects. Health researchers pursuing stakeholder-based projects in other contexts are encouraged to incorporate this process of stakeholder identification and categorization to ensure systematic consideration of relevant perspectives in their work.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.288
GPT teacher head0.430
Teacher spread0.141 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations123
Published2013
Admission routes2
Has abstractyes

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