Transitions in health service use among women with poor mental health: a 7-year follow-up
Bibliographic record
Abstract
OBJECTIVE: Women suffering from mental health problems require varied needs of mental health service utilisation. Transition between general practitioner and mental health services use are available through the Better Access Scheme initiative, for those in need of treatment. The study's aim was to identify trajectories of mental health service utilisation by Australian women. DESIGN: The Australian Longitudinal Study on Women's Health data linked to the administrative medical claims dataset were used to identify subgroups of women profiled by their mental health service use from 2006 to 2013. Latent growth mixture model is a statistical method to profile subgroups of individuals based on their responses to a set of observed variables allowing for changes over time. Latent class groups were identified, and used to examine predisposing factors associated with patterns of mental health service use change over time. SETTING: This study was conducted in Australia. PARTICIPANTS: National representative sample of women of born in 1973-1978, who were aged between 28 and 33 years at the start of our study period. RESULTS: Six latent class trajectories of women's mental health service use were identified over the period 2006-2013. Approximately, one-quarter of the sample were classified as the most recent users, while approximate equal proportions were identified as either early users, late/low user or late-high users. Additional, subgroups were defined as the consistent-reduced user and the late-high users, over time. Only 7.2% of the sample was classified as consistent high users who potentially used the services each year. CONCLUSION: These findings suggest that use of the Better Access Scheme mental health services through primary care was varied over time and may be tailored to each individual's needs for the treatment of depressive symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".