Humanising childbirth – Maternity acupressure training for healthcare providers at the Fernandez Foundation Hospitals, Hyderabad, India. Evaluation of program delivery in one region of India
Bibliographic record
Abstract
BACKGROUND: Internationally, traditional medicine approaches are used to support humanised childbirth practices. Labour support issues in low- and middle-income countries (LMICs), include limited resources, staffing, and escalating pharmaceutical interventions. There is a strong interest in evidence-based acupressure programs, however, training and experience to implement them is limited. Maternity professionals at the Fernandez Foundation (FF) including associated hospitals in the Hyderabad region, India, sought training in acupressure to support humanised childbirth. AIMS: To evaluate the implementation of the 'Acupressure for childbirth training program' at FF hospitals, up to 6-months post-training, including barriers and facilitators, as well as determining pregnancy and labour conditions for which the techniques are most useful. METHODS: Pre- and post-training, and 6-month surveys, were distributed to participants. RESULTS: Participants included a diverse group of 88 midwives, doulas, physiotherapists, educators and obstetricians. There were significant improvements in participant skills and knowledge, which persisted up to 6-months post-training (p<0.01). Participants indicated they were 'highly satisfied' with the training, and found it valuable, easy to implement, and reported extremely positive responses from women and support people. Facilitators to implementation included 'strategies and ideas', 'effectiveness of pain relief', and 'aiding labour progress'. Barriers included 'other staff and institutional challenges', 'needing more training', 'women's attitudes'. CONCLUSION: Acupressure training as part of a humanised approach to childbirth, demonstrates significant skill and knowledge gain, usefulness of training and skills, ease of implementation, and a highly positive reception within the clinical environment. Implementation of these practices should be widespread and supported by policy makers and clinicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".