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Non-supportive interactions in the experience of women family caregivers

2007· article· en· W2141192166 on OpenAlexafffund
Anne Neufeld, Margaret J. Harrison, Karen D. Hughes, Moira Stewart

Bibliographic record

VenueHealth & Social Care in the Community · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsCanadian Institutes of Health ResearchInstitute of Gender and HealthUniversity of Alberta
FundersInstitute of Gender and HealthCanadian Institutes of Health ResearchHealth Canada
KeywordsThematic analysisPsychologyTypologyContext (archaeology)DementiaFamily caregiversContent analysisDevelopmental psychologyClinical psychologyQualitative researchDiseaseMedicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.411
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2007
Admission routes2
Has abstractyes

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