The Uneven Path of Psychological Distress: How Socioeconomic Status Shapes Distress Transitions in Australian Adults
Bibliographic record
Abstract
OBJECTIVES: Psychological distress (PD) is a major public health concern linked to progressive disability. Estimating PD transition probabilities is essential for guiding targeted interventions and policies, particularly in the context of health economic models used for pharmaceutical reimbursement. We aimed to quantify how transition probabilities vary by socioeconomic status across 4 health states of PD (no PD, mild PD, moderate PD, and severe PD) among Australian adults with PD. METHODS: We obtained data on PD status and socioeconomic characteristics from the Household, Income and Labour Dynamics in Australia survey (2007-2021). We used a 4-state continuous-time Markov model to describe annual transitions between PD states. The model allowed tracking of both forward and backward transitions, enabling movement between any pair of states, including persistence of the same state. Socioeconomic status was assessed using education, employment, and income. RESULTS: Overall, 25 232 participants were identified for the study. The highest probabilities of worsening were for transitioning from mild PD to moderate PD (16.9%), whereas the highest probability of improvement was recorded for the transition from moderate to mild PD (32.3%). Higher recovery rates from severe PD to no PD were observed among individuals with higher education levels than in those with lower education (8.6% vs 5.8%), among the employed compared with the unemployed (7.0% vs 4.8%), and among those in the highest income bracket compared with those in the lowest (12.7% vs 5.7%). CONCLUSIONS: The estimated transition probabilities can be used in health economic evaluations, designed to support reimbursement decision for interventions.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".