Understanding childbirth practices as an organizational cultural phenomenon: a conceptual framework
Bibliographic record
Abstract
Understanding the main values and beliefs that might promote humanized birth practices in the specialized hospitals requires articulating the theoretical knowledge of the social and cultural characteristics of the childbirth field and the relations between these and the institution. This paper aims to provide a conceptual framework allowing examination of childbirth practices through the lens of an organizational culture theory. A literature review performed to extrapolate the social and cultural factors contribute to birth practices and the factors likely overlap and mutually reinforce one another, instead of complying with the organizational culture of the birth place. The proposed conceptual framework in this paper examined childbirth patterns as an organizational cultural phenomenon in a highly specialized hospital, in Montreal, Canada. Allaire and Firsirotu's organizational culture theory served as a guide in the development of the framework. We discussed the application of our conceptual model in understanding the influences of organizational culture components in the humanization of birth practices in the highly specialized hospitals and explained how these components configure both the birth practice and women's choice in highly specialized hospitals. The proposed framework can be used as a tool for understanding the barriers and facilitating factors encountered birth practices in specialized hospitals.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".