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Record W4283312480 · doi:10.60967/healthnz.29641199

Measuring health consumers’ engagement at the governance level: development and validation of the Middlemore Consumer Engagement Questionnaire

2025· article· en· W4283312480 on OpenAlexaboutno aff
Karol Czuba, Christin Coomarasamy, Richard J. Siegert, R A Ley Greaves, Lucy Wong, Te Hao Apaapa-Timu, Lynne Maher

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaFocus groupLikert scaleFace validityBenchmarkingMedicineApplied psychologyRelevance (law)Reliability (semiconductor)Corporate governancePsychologyMedical educationPsychometricsClinical psychologyMarketingBusiness

Abstract

fetched live from OpenAlex

AIM: To develop and validate a questionnaire to measure health CE at governance level. METHOD: This study used qualitative and quantitative methods (including focus groups, cognitive interviews and an international survey), and consisted of two phases. In Phase 1, an initial list of items was generated and refined with feedback from health consumer representatives. In Phase 2, a draft survey was distributed to n=227 consumers from New Zealand, Australia and Canada. The benefit and relevance of using the questionnaire was explored through face-to-face interviews with five CE leaders from New Zealand healthcare organisations. RESULTS: The proposed questionnaire comprises 25 statements relating to CE. Respondents indicate their level of agreement with the statements on a five-point Likert-type scale. Focus group and cognitive interview participants found the questionnaire relevant and easy to understand. The questionnaire scores correlated with the PPEET, another instrument measuring consumer engagement, and showed excellent internal consistency (Cronbach's alpha=0.97), unidimensionality and test-retest reliability (r=0.84). CONCLUSION: The proposed questionnaire measures CE at governance level and can be used for international comparisons and benchmarking. It showed sound psychometric properties and its value and relevance was recognised by health consumer representatives and leaders with CE roles in New Zealand healthcare organisations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.104
GPT teacher head0.243
Teacher spread0.139 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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