Exploring the reliability and profile of frequent mental health presentations using different methods: An observational study using statewide ambulance data over a 4-year period
Bibliographic record
Abstract
INTRODUCTION: A disproportionate number of mental health presentations to emergency services are made by frequent presenters. No current consensus definition of a frequent presenter exists. Using a statewide population-based ambulance database, this study (i) applied previous statistical methods to determine thresholds for frequent presenters, (ii) explored characteristics of the identified frequent presenter groups compared to non-frequent presenters and (iii) assessed the reliability of these methods in predicting continued frequent presenter status over time. METHODS: Statistical methods utilised in previous studies to identify frequent presenters were applied to all ambulance attendances for mental health symptoms, self-harm and alcohol and other drug issues between 1 January 2017 and 31 December 2020 in Victoria, Australia. Differences in characteristics between identified frequent and non-frequent presenter groups were determined by logistic regression analysis. The consistency of agreement of frequent presenter status over time was assessed using intraclass correlation coefficients. RESULTS: Thresholds for frequent presenters ranged from a mean of 5 to 39 attendances per calendar year, with groups differing in size, service use and characteristics. Compared to non-frequent presenters, frequent presenters had greater odds of being female, presenting with self-harm, experiencing social disadvantage or housing issues, involving police co-attendance and being transported to hospital. All frequent presenter definitions had poor reliability in predicting ongoing frequent presentations over time. CONCLUSION: A range of methods can define frequent presenters according to thresholds of yearly service use. Reasons for identifying frequent presenters may influence the method chosen. Future studies should explore definitions that capture the dynamic nature of presentations by this group.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".