The facilitating factors and barriers encountered in the adoption of a humanized birth care approach in a highly specialized university affiliated hospital
Bibliographic record
Abstract
BACKGROUND: Considering the fact that a significant proportion of high-risk pregnancies are currently referred to tertiary level hospitals; and that a large proportion of low obstetric risk women still seek care in these hospitals, it is important to explore the factors that influence the childbirth experience in these hospitals, particularly, the concept of humanized birth care.The aim of this study was to explore the organizational and cultural factors, which act as barriers or facilitators in the provision of humanized obstetrical care in a highly specialized, university-affiliated hospital in Quebec province, in Canada. METHODS: A single case study design was chosen. The study sample included 17 professionals and administrators from different disciplines, and 157 women who gave birth in the hospital during the study. The data was collected through semi-structured interviews, field notes, participant observations, a self-administered questionnaire, documents, and archives. Both descriptive and qualitative deductive content analyses were performed and ethical considerations were respected. RESULTS: Both external and internal dimensions of a highly specialized hospital can facilitate or be a barrier to the humanization of birth care practices in such institutions, whether independently, or altogether. The greatest facilitating factors found were: caring and family- centered model of care, professionals' and administrators' ambient for the provision of humanized birth care besides the medical interventional care which is tailored to improve safety, assurance, and comfort for women and their children, facilities to provide a pain-free birth, companionship and visiting rules, dealing with the patients' spiritual and religious beliefs. The most cited barriers were: the shortage of health care professionals, the lack of sufficient communication among the professionals, the stakeholders' desire for specialization rather than humanization, over estimation of medical performance, finally the training environment of the hospital leading to the presence of too many health care professionals, and consequently, a lack of privacy and continuity of care. CONCLUSION: The argument of medical intervention and technology at birth being an opposing factor to the humanization of birth was not seen to be an issue in the studied highly specialized university affiliated hospital.
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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".