Dataset linking women's maternity care experiences with hospital environment and governance in Ireland
Bibliographic record
Abstract
Many scholars argue that there is a deepening crisis of trust in healthcare systems. What is not contested is the centrality of public trust in building reputational value in healthcare organisations. However, there is a dearth of research focused on better understanding how trust in healthcare institutions, and the healthcare workforce, can be sustainably cultivated. To enable the exploration of care-related factors within hospitals and their potential impacts on trust in healthcare workers, this dataset was created based on the 2020 National Maternity Experience Survey data. The survey data include responses to 68 structured, tick-box questions and three open-ended questions prepared with the participation of over 250 healthcare practitioners and experts, patients, as well as policymakers and researchers. The survey covers the full pathway of maternity care from antenatal care, through labour and birth, to postnatal care in the community. A total of 19 maternity hospitals and units participated in the survey which ran from February to April 2020, resulting in a total of 3204 women responses out of an eligible population of 6357. The survey data was extended with contextual information from a monitoring report on the National Maternity Services Standard published in 2020. The additional data includes compliance levels of maternity hospitals with established standards in four key areas including effective care support, safe care support, leadership governance and management, and workforce. This curated dataset can support investigations into a) the factors that determine overall women's care experience, b) factors contributing to building confidence and trust in the maternity care workforce among different groups of women, and c) how hospital environment, processes and governance impact both women's trust in maternity hospitals and their overall satisfaction.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".