Therapeutic Landscapes of Home: Exploring Women’s Perceptions and Experiences of Home as a Place of Birth in London, Ontario
Bibliographic record
Abstract
Home birth is a very controversial issue in today’s research literature. Using a therapeutic landscapes approach, this thesis focuses on the perception and experiences of home as a place of birth in London, Ontario, Canada. Thirty interviews were conducted during the summer of 2014 with women who lived in the London area and either planned a home birth in the previous two years or were currently pregnant and planning a home birth. Results demonstrated that women chose home birth, and did not choose hospital birth, due to the need for comfort, control, and support. Key themes around barriers included overwhelming criticism, a lack of information, not enough midwives, and the absence of an alternate choice in birth location. Findings from this study contribute to the literature by providing a unique geographical perspective to women’s health. Policy applications range from the local to federal level and involve such elements as providing a more relaxing and welcoming hospital environment for women; knowledge mobilization within the health care community; and, further acceptance of alternative locations of birth within the Ontario and Canada contexts. Future research should seek to grow the breadth of understanding of the perceptions of home as a place of birth. Similar studies at municipal levels or longitudinal studies of women’s experience pre- and post-birth could fill certain gaps in the literature and give further credence to women’s voices as key elements in shaping the health care system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".