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Development and initial psychometric validation of the advisor experience survey.

2018· article· en· W2892891221 on OpenAlexaffabout
Erica Bridge, Naomi Peek, Yvonne Leung, Lucia Vanta, Suman Dhanju, Simron Singh, Lesley Moody

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreCancer Care Ontario
Fundersnot available
KeywordsConfirmatory factor analysisExploratory factor analysisLikert scaleStructural equation modelingDelphi methodScale (ratio)Construct validityConstruct (python library)PsychologyPublic engagementMedicineApplied psychologyPsychometricsClinical psychologyDevelopmental psychologyPublic relationsComputer science

Abstract

fetched live from OpenAlex

220 Background: Patient, family and public engagement builds strong, sustainable partnerships benefitting the planning, delivery and evaluation of health services. With growing international evidence and increased organizational investment supporting this type of engagement, evaluating the experience of these individuals is essential to ensuring meaningful participation and value. This study describes the development and psychometric properties of the Advisor Experience Survey in order to assess on-going patient, family and public advisor engagement at the system-level. Methods: The development and psychometric validation consisted of five steps: 1) literature review to identify relevant engagement experience items; 2) modified Delphi process where relevant engagement experience items were rated by experts; 3) cognitive interviews to ensure that items were clear and understandable; 4) pilot of survey; and 5) exploratory and confirmatory factor analysis (EFA; CFA) to determine construct validity. Results: The 13-item survey was disseminated to eligible patient, family and public advisors between April and September 2016 using FluidSurveys. All items were rated on a 5-point Likert scale. A total of 126 responses were included in the EFA and CFA. The final confirmatory solution consists of 3-factors (Support and Resources, Engagement Opportunities and Engagement Experience), including 9-items (X2= 31.4, df = 24; AGFI = 0.92; CFI = 0.99; SRMR = 0.02 and RMSEA = 0.05; 95% C.I. = 0.00-0.09). The 3-factor solution explains a total of 92% of the variance of the advisor engagement experience. Conclusions: The 3-factor Advisor Experience Survey with 9-items is acceptable for measuring the patient, family and public advisor engagement experience as it demonstrates good internal consistency and construct reliability. Cancer Care Ontario utilizes the 3-factor solution to report patient, family and advisor engagement experience at the system-level on a quarterly basis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.485
GPT teacher head0.641
Teacher spread0.157 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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