Psychometric properties of the “Autonomous and Controlled Motivation for Treatment Questionnaire” in women with eating disorders
Bibliographic record
Abstract
OBJECTIVE: To validate the Autonomous and Controlled Motivation for Treatment Questionnaire (ACMTQ) for use in women with an eating disorder (ED). METHOD: Data were available for 463 individuals. We assessed factor structure, internal reliability, test-retest reliability, convergent/divergent validity, and incremental predictive validity. RESULTS: Our data showed acceptable fit to our hypothesized model (comparative fit index = 0.92, root mean square error of approximation = 0.09, standardized root mean square residual = 0.09). We found test-retest reliability of 0.73 for both the autonomous (α = 0.85) and controlled (α = 0.80) subscales. Autonomous scores were more strongly associated with motivation measures (β = 0.37; 0.46) than with ED severity measures (β = -0.10; -0.18). Associations between autonomous motivation and symptom improvement over time supported predictive validity. Controlled motivation was associated with lower motivation (β = 0.02; -0.31) and with higher ED severity (β = 0.12; 0.47). CONCLUSION: Our results suggest that the ACMTQ is valid for use in women with EDs and lend support to the validity of findings from previous ED studies that have used the ACMTQ.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".