MétaCan
Menu
Back to cohort
Record W2935496219 · doi:10.3138/jcfs.31.3.367

Women Caring for Elderly Family Members: Shaping Non-Traditional Work and Family Initiatives

2000· article· en· W2935496219 on OpenAlexvenueno aff
Judy L. Singleton

Bibliographic record

VenueJournal of Comparative Family Studies · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsEthnic groupAttendancePsychologyWork (physics)ProductivityGerontologySociologyPolitical scienceMedicineEconomic growthBusiness

Abstract

fetched live from OpenAlex

Caring for a dependent elderly family member and employment are competing demands for men, and especially women, who work in the United States. Women traditionally function in the caregiving role for parents in need. Yet unlike their mothers before them, modem day women caring for elderly parents have more roles, and thus more role demands upon them. Traditional familial roles as wives, homemakers, and mothers are more often coupled with roles as paid workers and as caregiving daughters to dependent parents. Maintaining today’s families via the kin-keeping role may be increasingly difficult for the employed elder caregiver, typically a female in her mid-40s to late 50s. The burdens employed caregivers experience have effects in the workplace, for example, attendance problems and poor job performance. Yet, the research in this area is relatively sparse, especially in data on the effect of family status on numerous measures of productivity and employee performance. How research can deliver assistance in programming and policy development of eldercare benefits in the workplace by demonstrating which categories of employees (i.e., by gender, age, race, ethnicity, occupational status, and earnings) are most affected by elder caregiving and in what ways is presented. Family and work initiatives to help maintain this balance between family and job responsibilities are discussed and recommended.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.210
GPT teacher head0.379
Teacher spread0.169 · 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

Citations36
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Comparative Family StudiesSame topicWork-Family Balance ChallengesFrench-language works237,207