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Record W2100245268 · doi:10.1002/nur.21504

“Flying by the seat of our pants”: What dementia family caregivers want in an advanced caregiver training program

2012· article· en· W2100245268 on OpenAlexaff
Linda Samia, Kenneth Hepburn, Lynne Nichols

Bibliographic record

VenueResearch in Nursing & Health · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCollege & Association of Registered Nurses of AlbertaRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsDementiaCaregiver stressFamily caregiversPsychological interventionFocus groupIntervention (counseling)PsychologyGerontologyResource (disambiguation)Caregiver burdenNursingMedicineDisease

Abstract

fetched live from OpenAlex

Although dementia caregiver stress and burden can be ameliorated with intervention, such interventions typically occur in specific caregiving contexts and at specific points in the progression of the dementing illness. We explored the ongoing learning needs and preferences of previously trained caregivers. Descriptive analysis of caregiver survey responses (N = 168) informed follow-up focus group interviews conducted with 26 family caregivers. Content analysis of focus group data showed that caregivers enjoyed the interactive problem-solving of the group related to preparing for the future, shaping the troubling behavior of the care recipient, reshaping the resource team, and caring for themselves. These findings support the need for advanced training as caregivers move through the trajectory of caring for persons with dementia.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.150
GPT teacher head0.509
Teacher spread0.359 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations54
Published2012
Admission routes1
Has abstractyes

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