Behavioural factors associated with fear of litigation as a driver for the increased use of caesarean sections: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To explore the behavioural drivers of fear of litigation among healthcare providers influencing caesarean section (CS) rates. DESIGN: Scoping review. DATA SOURCES: We searched MEDLINE, Scopus and WHO Global Index (1 January 2001 to 9 March 2022). DATA EXTRACTION AND SYNTHESIS: Data were extracted using a form specifically designed for this review and we conducted content analysis using textual coding for relevant themes. We used the WHO principles for the adoption of a behavioural science perspective in public health developed by the WHO Technical Advisory Group for Behavioural Sciences and Insights to organise and analyse the findings. We used a narrative approach to summarise the findings. RESULTS: We screened 2968 citations and 56 were included. Reviewed articles did not use a standard measure of influence of fear of litigation on provider's behaviour. None of the studies used a clear theoretical framework to discuss the behavioural drivers of fear of litigation. We identified 12 drivers under the three domains of the WHO principles: (1) cognitive drivers: availability bias, ambiguity aversion, relative risk bias, commission bias and loss aversion bias; (2) social and cultural drivers: patient pressure, social norms and blame culture and (3) environmental drivers: legal, insurance, medical and professional, and media. Cognitive biases were the most discussed drivers of fear of litigation, followed by legal environment and patient pressure. CONCLUSIONS: Despite the lack of consensus on a definition or measurement, we found that fear of litigation as a driver for rising CS rates results from a complex interaction between cognitive, social and environmental drivers. Many of our findings were transferable across geographical and practice settings. Behavioural interventions that consider these drivers are crucial to address the fear of litigation as part of strategies to reduce CS.
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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 | 0.019 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".