Factors associated with initiation of community‐based therapy for emerging adults with mood and anxiety disorders
Bibliographic record
Abstract
AIM: The First Episode Mood and Anxiety Program (FEMAP) is a community-based early intervention program that has been shown to improve health outcomes for emerging adults (EAs) with mood and anxiety disorders. However, not all EAs who are admitted to the program initiate treatment. Our aim was to identify factors that distinguish those who initiated treatment from those who did not. METHODS: FEMAP administered questionnaires to EAs upon first contact with the program, collecting information on a range of socioeconomic, patient and condition-related factors. We compared EAs who initiated treatment in the program (n = 318, 87.4%) to those who did not (n = 46, 12.6%). To examine factors associated with treatment initiation, we specified a parsimonious logistic regression model, using the method of purposeful selection to choose from a range of candidate variables. RESULTS: Anxiety Sensitivity Index - Revised (ASI-R), binge drinking and cannabis use were included in the final logistic regression model. Each one-point increment in the ASI-R score was associated with a 1% increase in the odds of treatment initiation (OR = 1.014; 95% CI [1.003, 1.026]). No other variable was significantly associated with treatment initiation. CONCLUSIONS: Our study provides insight on the differences between EAs with mood and anxiety disorders who initiated targeted treatment services and those who did not. Anxiety sensitivity was significantly associated with treatment initiation at FEMAP. Our findings suggest that it may be anxiety sensitivity, rather than depression or functional impairment per se that drive treatment initiation among EAs.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".