Factors associated with reported service use for mental health problems by residents of rural and remote communities: cross-sectional findings from a baseline survey
Bibliographic record
Abstract
BACKGROUND: The patterns of health service use by rural and remote residents are poorly understood and under-represented in national surveys. This paper examines professional and non-professional service use for mental health problems in rural and remote communities in Australia. METHODS: A stratified random sample of adults was drawn from non-metropolitan regions of New South Wales, Australia as part of a longitudinal population-based cohort. One-quarter (27.7%) of the respondents were from remote or very remote regions. The socio-demographic, health status and service utilization (professional and non-professional) characteristics of 2150 community dwelling residents are described. Hierarchical logistic regressions were used to identify cross-sectional associations between socio-demographic, health status and professional and non-professional health service utilization variables. RESULTS: The overall rate of professional contacts for mental health problems during the previous 12 months (17%) in this rural population exceeded the national rate (11.9%). Rates for psychologists and psychiatrists were similar but rates for GPs were higher (12% vs. 8.1%). Non-professional contact rates were 12%. Higher levels of help seeking were associated with the absence of a partner, poorer finances, severity of mental health problems, and higher levels of adversity. Remoteness was associated with lower utilization of non-professional support. A Provisional Service Need Index was devised, and it demonstrated a broad dose-response relationship between severity of mental health problems and the likelihood of seeking any professional or non-professional help. Nevertheless, 47% of those with estimated high service need had no contact with professional services. CONCLUSIONS: An examination of self-reported patterns of professional and non-professional service use for mental health problems in a rural community cohort revealed relatively higher rates of general practitioner attendance for such problems compared with data from metropolitan centres. Using a measure of Provisional Service Need those with greater needs were more likely to access specialist services, even in remote regions, although a substantial proportion of those with the highest service need sought no professional help. Geographic and financial barriers to service use were identified and perception of service adequacy was relatively low, especially among those with the highest levels of distress and greatest adversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".