Identifying Psychological Distress Data Available in Nationally Representative Surveys: A Scoping Review and Case Study of Australian Surveys
Bibliographic record
Abstract
AbstractPurpose: Mental health data are crucial for understanding trends in psychological distress. This scoping review aimed to identify and describe surveys of representative samples of the Australian household population that measured psychological distress, and to provide a case study illustrating how datasets can be systematically summarized to assist researchers to more easily identify available datasets.Methods: We systematically searched PubMed and data archives for surveys state or nationally representative of the Australian household population that assessed psychological distress.Results: We provide a searchable metadata database characterizing 282 identified datasets from 41 studies (25 cross-sectional, 16 longitudinal) conducted between 1989 and 2023. Forty psychological distress instruments were used, with the Kessler Psychological Distress scale [1] most common (n = 113 datasets). Surveys also frequently measured demographics, physical health, and socioeconomic information. Stratified random sampling of geographic areas was the most common sampling frame, and adults the most frequently sampled group. There was notably less representation of important subgroups of the population, including youth, Aboriginal and Torres Strait Islander people, and people with disabilities, despite evidence of high distress prevalence in these groups. Conclusions: This review provides valuable metadata summarizing available psychological distress datasets, including information on sampling designs, instrumentation, and covariates. This metadata is available to other researchers, enabling efficient identification of relevant datasets, promoting data sharing, and supporting future data integration. This method for systematically compiling metadata can be replicated for data related to other topics important to public health to facilitate greater data utilization.
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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 | 0.139 | 0.448 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.047 | 0.052 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 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".