Quality indicators for rural surgical and obstetrical care: A modified Delphi consensus study
Bibliographic record
Abstract
OBJECTIVE: To identify contextually relevant indicators to measure the quality of surgical and obstetrical care in low-volume rural hospitals using a consensus-based methodology. METHODS: A modified Delphi process was implemented in which participants were asked to rate the priority of proposed evaluation metrics over two rounds. Two Delphi surveys were electronically administered in 2019, approximately one month apart. Fifty-one health care professionals from across Canada, including rural proceduralists and quality improvement experts, were invited to participate. All quality measures in the first round were proposed by the study team. The second round included measures that did not reach consensus in the first round and measures suggested by respondents during the first round. RESULTS: Thirty individuals participated in Round 1 (59% response rate). Of the 30 respondents from Round 1, 23 participated in Round 2 (77% response rate). 115 of 177 proposed measures (65%) reached positive consensus in Round 1 or 2. Expert participants agreed that these measures should be prioritized/included when evaluating surgical and/or obstetrical quality in rural hospitals. No measure reached negative consensus in either round. Open-text comments offered practical guidance on how to interpret and use surgical and obstetrical quality data within a rural context. Many respondents believed that rare adverse outcomes have low relevance at rural hospitals where volumes are low, procedures are almost all lower complexity day cases (Cesarean section being the major exception), and patients are typically healthy. CONCLUSION: The modified Delphi process resulted in the identification of surgical and obstetrical quality indicators that are contextually embedded in the realities of rural practice. The methodology allowed for the consideration of factors often overlooked by normative urban-based approaches, including team-based care characteristic of rural hospitals and limited access to specialist care and imaging 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 | 0.184 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".