Keeping close: mothering with serious mental illness
Bibliographic record
Abstract
AIM: The aim of this paper is to describe the experiences of mothers with serious mental illness from their perspectives and how they attempted to manage their mothering circumstances. BACKGROUND: The desire to mother in women with serious mental illness is increasingly acknowledged by healthcare professionals. For these women, mothering is often framed as a pathological problem needing professional intervention. Yet little is known about mothering and illness from the perspectives of the mothers themselves. METHOD: Using Glaser's grounded theory approach and both purposive and theoretical sampling, interviews were conducted with 20 mothers who were receiving treatment for mental health problems. The data were collected in 2002. FINDINGS: We found the core category of Keeping close described mothers' efforts to have meaningful relationships with their children in the context of illness and suffering. To this end, mothers chose strategies that would hide illness for the sake of protecting their roles and their children. These strategies--masking, censoring speech, doing motherwork and seeking help--served to imitate ideal perceptions of mothering while making illness invisible to their children. Mothering in illness, however, became a vortex of contradictions, resulting in mothers 'hitting bottom', a point in time when they realized they could not keep close via pretences. To return to the valued place of mother, they sought treatment, hoping to learn how to be with their children authentically. CONCLUSION: To assist mothers with serious mental illness, healthcare professionals must be sensitive to the social and cultural context in which they mother in order to create healthier possibilities for nurturing their children.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".