A Mother’s Voice: The Construction of Maternal Identity Following Perinatal Loss
Bibliographic record
Abstract
BackgroundMaternal identity, a mother's internalized view of self as mother, has not been studied in relation to perinatal loss. This study aimed to investigate how women construct a sense of maternal identity after the loss of a baby.MethodsWe interviewed 10 mothers who had experienced perinatal loss. A Listening Guide framework for narrative analysis was used to identify patterns of giving voice to the mother's own story.ResultsWe identified 12 overarching voices which fell within three distinct groupings: voices of motherhood, voices of grief, and voices of growth. Although bereaved mothers grappled with constructing their maternal identity, they also demonstrated how maternal identity is individually and intuitively created through an honouring and remembering of the child that was lost, resulting in significant growth.ConclusionsThere is need for a broader definition of what constitutes motherhood to encapsulate diverse mothering experiences, including perinatal loss.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".