MétaCan
Menu
Back to cohort
Record W2052748848 · doi:10.1188/08.cjon.501-506

Caring for Bereaved Family Caregivers: Analyzing the Context of Care

2008· review· en· W2052748848 on OpenAlexaff
Lorraine Holtslander

Bibliographic record

VenueClinical journal of oncology nursing · 2008
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGriefMedicineFamily caregiversPalliative careNursingContext (archaeology)PopulationSocial supportQuality of life (healthcare)Health carePsychiatryPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Deaths from cancer will continue to rise with an increasing and aging population. Family caregivers of patients with cancer will face loss, grief, and bereavement as a result. As mandated by cancer and palliative care clinical practice guidelines, support for family caregivers continues through the processes of grief and bereavement to facilitate a positive transition through loss. To provide evidence-based nursing with this population, an analysis of their context of care was undertaken. Key health policies, characteristics of the healthcare delivery system, and the results of research with bereaved palliative caregivers are described. A model of effectiveness, efficiency, and equity is used to examine the situation of bereaved caregivers and to suggest research questions to fill the gaps in what is known about their needs and experience. Bereaved caregivers are at high risk for many distressing symptoms, including depression and sleeplessness, related to a range of complex variables, such as age, gender, social support, resources, and their experiences during caregiving. Current systems of support have not been adequate to meet the needs of this population and very little is known about the caregivers' quality of life, well-being, and health outcomes or how best to provide compassionate and effective nursing care.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.422
GPT teacher head0.584
Teacher spread0.163 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations41
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueClinical journal of oncology nursingSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207