The Well-Being Coaching Inventory (WCI): Questionnaire Development and Validation
Bibliographic record
Abstract
Objective: The aim of the present study was to psychometrically test and validate the Well-being Coaching Inventory (WCI), a proposed measure of interconnected, whole-person well-being in the context of health and wellness coaching (HWC). Methods: Initially 49 items, the WCI was conceived with 4 dimensions: Mind, Body, Work, and Life. The inventory was evaluated in 3 sequential studies to test: (a) face validity, (b) convergent validity, and (c) predictive validity. Expert judgment, correlational analyses, and factor analyses were techniques applied to collected WCI data. Results: After statistical evaluation (n = 261) of fit to each dimension, the WCI was shortened to 20 items that demonstrated convergent validity. Further use of confirmatory factor analyses and exploratory structural equation model in a large sample study (n = 531) provided additional support for the inventory's convergent validity. Through correlation analyses to theoretically related concepts predictive validity was established. Conclusions: The WCI is a valid, applicable, and reliable scale for use in HWC research and practice. It is an instrument that will aid HWC practitioners and researchers as a central outcome measure for their practice.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".