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Record W2100049180 · doi:10.1177/1074840714562645

Nurses Negotiating Professional–Familial Care Boundaries

2014· article· en· W2100049180 on OpenAlexafffund
Catherine Ward‐Griffin, Judith Belle Brown, Oona St-Amant, Nisha Sutherland, Anne Martin-Matthews, Janice Keefe, Mickey Kerr

Bibliographic record

VenueJournal of Family Nursing · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsLakehead UniversityUniversity of British ColumbiaToronto Metropolitan UniversityMount Saint Vincent UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsNegotiationDutyDialecticBalance (ability)NursingTollPsychologyHealth careWork (physics)MedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

The purpose of this sequential, two-phase mixed-methods study was to examine the health of male and female nurses who provided care to older relatives (i.e., double duty caregivers). We explored the experiences of 32 double duty caregivers, which led to the development of an emergent grounded theory, Negotiating Professional-Familial Care Boundaries with two broad dialectical processes: professionalizing familial care and striving for balance. This article examines striving for balance, which is the process that responds to familial care expectations in the midst of available resources and reflects the health experiences of double duty caregivers. Two subprocesses of striving for balance, reaping the benefits and taking a toll, are presented in three composite vignettes, each representing specific double duty caregiving (DDC) prototypes (making it work, working to manage, living on the edge). This emergent theory extends current thinking of family caregiving that will inform the development and refinement of practices and policies relevant to DDC.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.062
GPT teacher head0.418
Teacher spread0.356 · 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 designNot applicable
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

Citations51
Published2014
Admission routes2
Has abstractyes

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