Psychometric evaluation of the treatment entry questionnaire to assess extrinsic motivation for inpatient addiction treatment
Bibliographic record
Abstract
Valid multi-faceted measurement of motivation for substance use disorder (SUD) treatment is needed to help inform treatment approaches and predict outcomes. This study examined evidence of validity for the Treatment Entry Questionnaire (TEQ-9). Data represented individuals entering inpatient SUD treatment (n = 1455). We used confirmatory factor analysis (CFA) to assess the three-factor structure of the TEQ-9 [identified (i.e., values/personally chooses treatment), introjected (i.e., internally controlled by guilt/shame) and external motivations (i.e., external pressure/demands)], and examined measurement invariance across gender, age, and ethno-racial identity. Correlation with readiness and confidence assessed convergent validity, while correlations with substance use problem severity and previous substance use treatment assessed meaningful group differences. A three-factor structure was confirmed with all items loading significantly onto their respective factors (ps < 0.001). Each subscale demonstrated high internal consistency (Identified α = 0.90; Introjected α = 0.79; External α = 0.85). Each subscale demonstrated measurement invariance up to the scalar level across all sub-groups. Readiness, confidence, and substance use problem severity correlated as expected across various substances with the identified (rs = 0.098 — 0.262, ps < 0.05), and external (rs = -0.096 — -0.178, ps < 0.05) subscales. Additionally, the mean Identified subscale score was significantly higher among those who previously engaged in SUD treatment (p < 0.001). Findings for the Introjected subscale were more ambiguous. Findings provide evidence for factorial validity, measurement invariance, convergent validity and group differences of the TEQ-9 in a large clinically mixed inpatient SUD treatment population, providing further support of its clinical and research utility.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".