Reconceptualising risk: Perceptions of risk in rural and remote maternity service planning
Bibliographic record
Abstract
OBJECTIVE: to explore perceptions and examples of risk related to pregnancy and childbirth in rural and remote Australia and how these influence the planning of maternity services. DESIGN: data collection in this qualitative component of a mixed methods study included 88 semi-structured individual and group interviews (n=102), three focus groups (n=22) and one group information session (n=17). Researchers identified two categories of risk for exploration: health services risk (including clinical and corporate risks) and social risk (including cultural, emotional and financial risks). Data were aggregated and thematically analysed to identify perceptions and examples of risk related to each category. SETTING: fieldwork was conducted in four jurisdictions at nine sites in rural (n=3) and remote (n=6) Australia. PARTICIPANTS: 117 health service employees and 24 consumers. MEASUREMENTS AND FINDINGS: examples and perceptions relating to each category of risk were identified from the data. Most medical practitioners and health service managers perceived clinical risks related to rural birthing services without access to caesarean section. Consumer participants were more likely to emphasise social risks arising from a lack of local birthing services. KEY CONCLUSIONS: our analysis demonstrated that the closure of services adds social risk, which exacerbates clinical risk. Analysis also highlighted that perceptions of clinical risk are privileged over social risk in decisions about rural and remote maternity service planning. IMPLICATIONS FOR PRACTICE: a comprehensive analysis of risk that identifies how social and other forms of risk contribute to adverse clinical outcomes would benefit rural and remote people and their health services. Formal risk analyses should consider the risks associated with failure to provide birthing services in rural and remote communities as well as the risks of maintaining services.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".