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Record W2129195017 · doi:10.1017/s1463423613000467

Development of a tool to investigate caregiving issues from the perspective of family physicians and discussion of preliminary results

2013· article· en· W2129195017 on OpenAlexafffundabout
Michel Bédard, Carrie Gibbons, Anik Lambert-Bélanger, Julie Riendeau

Bibliographic record

VenuePrimary Health Care Research & Development · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsLakehead UniversityNOSM UniversitySt. Joseph's Care Group
FundersCanada Research ChairsOntario Ministry of Health and Long-Term CareOntario Neurotrauma Foundation
KeywordsPerspective (graphical)Family medicineFamily caregiversFamily healthDementiaPsychologyAccountabilityNursingMedicineMedical educationFamily memberGerontologyDisease

Abstract

fetched live from OpenAlex

AIM: The aim of our study was to develop a survey for family physicians to better understand family physicians' beliefs, level of knowledge and sense of accountability regarding their support of informal (i.e., unpaid) caregivers of older adults. BACKGROUND: Seniors with dementia can be supported to 'age in place'. However, this requires assistance from family and friends, who are often seniors themselves and may have health issues of their own. Although family physicians are well positioned to assist older adult caregivers, there is a paucity of data regarding this role. METHODS: After a literature review, we created a questionnaire to examine these issues. It was reviewed by experts and, after revision, was appraised by health planners/decision makers and pre-tested with family physicians. A final questionnaire was created using this feedback. FINDINGS: The next important step would be to administer the questionnaire to Canadian family physicians using appropriate survey methodology.

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)0.055
metaresearch head score (Gemma)0.106
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.106
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.069
GPT teacher head0.430
Teacher spread0.362 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations17
Published2013
Admission routes3
Has abstractyes

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