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Record W3014025551 · doi:10.1097/jcn.0000000000000678

Supporting the Health and Well-being of Caregivers

2020· article· en· W3014025551 on OpenAlexaff
Karen Bouchard, Jane Brownrigg, B. Quinlan, Jean Bilodeau, Gloria Higdon, Heather Tulloch

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsCanadian Institute for Advanced ResearchSocial Sciences and Humanities Research CouncilMontreal Heart InstituteUniversity of Ottawa
Fundersnot available
KeywordsPsychologyNursingMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Caregivers contribute substantially to patients' management of and recovery from cardiovascular disease (CVD). Yet, the distress that many caregivers experience in this role continues to be underresearched and their needs undersupported. PURPOSE: Situated within a patient engagement framework and adapted from experience-based co-design guidelines, the process of developing a comprehensive caregiver support resource with joint contributions from caregivers and healthcare providers representing multiple disciplines is described. A discussion of the challenges encountered during the development of the caregiver support resource and recommendations for future sites embarking on co-design work are noted. CONCLUSION: Developing feasible and relevant approaches, such as informational support instruments, to meet the needs of the growing population of CVD caregivers is essential. CLINICAL IMPLICATIONS: Although co-design processes are often complex, take more time and resources to implement, and involve multiple levels of an organization and community than traditional practices, these efforts may help to improve healthcare quality to stem the burden of CVD.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.364
Teacher spread0.312 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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