Women’s perspectives of the fetal fibronectin testing process: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: In 2009 the Ontario Ministry of Health and Long Term Care funded the implementation of province-wide fetal fibronectin testing in Ontario hospitals. This paper reports results from the provincial evaluation that sought to describe the experience of fetal fibronectin testing from the perspective of women with symptoms of preterm labour. METHODS: A descriptive qualitative design was used, employing semi-structured telephone and face-to-face interviews with women who had fetal fibronectin testing. RESULTS: Five hospitals participated in recruiting women for the study and 17 women were interviewed. Women described their experiences of fetal fibronectin testing as an emotional process that moves from expecting, to feeling, to hoping for reassurance; and then to re-defining what is required to feel reassured. Women described feeling anxious while waiting for fetal fibronectin results. When test results were negative, women described feeling a sense of relief that their symptoms would not likely lead to an imminent preterm birth. Women with positive results expressed feeling reassured by the care decisions and quick action taken by the health care team. CONCLUSION: Fetal fibronectin testing was acceptable and beneficial to these women with symptoms of preterm labour. Implications for practice and future research are suggested.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".