Development and initial psychometric validation of the advisor experience survey.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".