Development and validation of the multidimensional Fear of Depression Recurrence Questionnaire (FoDRQ)
Bibliographic record
Abstract
Despite high recurrence rates in major depressive disorder (MDD), little is known about the factors influencing recurrence. Understanding the changes that occur between major depressive episodes (MDEs) is imperative. It is possible that being fearful of experiencing another MDE may lead to cognitive and behavioural changes that increase MDD recurrence risk. There are no available tools designed to capture these fears. This study developed and validated a self-report questionnaire measuring fears of depression recurrence (FoDR). 552 participants remitted from MDD (75% female; Age 18–73, Mage = 29.5, SD = 9.2) participated. Separate samples were used for the exploratory factor analysis (n = 200) and confirmatory factor analysis (n = 352). Test-retest reliability was assessed (n = 244). The results supported the retention of a 24-item scale, the Fear of Depression Recurrence Questionnaire (FoDRQ), loading onto three factors (severity, content, triggers). The FoDRQ demonstrated excellent internal consistency and composite reliability, and acceptable test-retest reliability. The scale showed strong convergent and divergent validity across other validated measures. FoDRQ scores significantly predicted measures of experiential avoidance and mental health self-management. The FoDRQ has excellent psychometric properties and can be used to understand how FoDR may influence MDD outcomes, recurrence risk, and illness-related coping.
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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 | 0.012 | 0.017 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".