Building confidence and trust in Ireland's National Maternity Services Workforce –What matters most and how?
Bibliographic record
Abstract
National surveys on care experiences are increasingly adopted as regulatory mechanisms for improving care quality and increasing public trust in healthcare services. Based on data collected as part of Ireland's 2020 National Maternity Experience Survey, this study investigates care-related factors that contribute most to confidence and trust in the professional workforce (or carers) within Irish maternity services. The survey covered the full spectrum of maternity care and received 3,206 responses which were analysed using structural equation modelling. Results show that trust in carers may be enhanced through greater attention to the quality of interpersonal aspects of maternity care in a few core areas. We found that factors related to dignity and respect (β=0.270), involvement in decision-making (β=0.186), pain management (β=0.172), and communication (β=0.151) are core determinants of confidence and trust in the professional workforce of maternity services. Perceived quality of care in these four aspects increased on average, with the women's age. Women under 29 rated their experiences in these areas as significantly lower than the average. Women with a disability also rated their experiences significantly lower than average in three core areas. Our results suggest that trustworthy, equitable, and high-quality maternity care requires ongoing development of interpersonal skills within the maternity services professional workforce particularly in caring for younger women (under 29 years) and those with a disability.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".