Non-supportive interactions in the experience of women family caregivers
Bibliographic record
Abstract
The purpose of this ethnographic study was to identify and describe types of non-supportive interactions perceived by 59 women family caregivers in four diverse situations. Participants included 15 mothers of infants born prematurely, 14 mothers of a child with a chronic disease (asthma or diabetes), and women caring for an adult family member with either cancer (15) or dementia (15). Data collection methods included an initial in-depth interview with all women, followed by a second interview with a smaller group of caregivers including a card sort exercise that was based on thematic content analysis of the first interview data. A typology of non-supportive interactions was developed from analysis of the first two interviews and confirmed in a final interview with a subset of study participants. Interviews were audio-taped and transcribed verbatim. Women in all caregiving situations described experience with three types of non-supportive interactions. These interactions were negative, ineffective, or lacking expected support. The women's appraisal of interactions as supportive or non-supportive was rooted in their personal expectations and the context of their situation. Information about types of non-supportive interactions can sensitise professionals, family and friends to mismatches between their assistance and caregivers' requirements, potentially avoiding negative consequences.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".