MétaCan
Menu
Back to cohort
Record W2341495472 · doi:10.1177/0733464815591213

Hoping for the Best or Planning for the Future: Decision Making and Future Care Needs

2015· article· en· W2341495472 on OpenAlexafffund
Odette N. Gould, Suzanne Dupuis‐Blanchard, Lita Villalón, Majella Simard, Sophie Éthier

Bibliographic record

VenueJournal of Applied Gerontology · 2015
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversité LavalUniversité de MonctonMount Allison University
FundersCanadian Institutes of Health ResearchMount Allison UniversityUniversity of Alberta
KeywordsPsychologyCoping (psychology)ResidenceGerontologyOlder peopleQualitative researchAdvance care planningNeeds assessmentPlan (archaeology)Applied psychologyNursingMedicineClinical psychologySociology

Abstract

fetched live from OpenAlex

Research has shown that relatively few older adults make plans for future care needs. In this study, we explore the thinking processes involved in planning or failing to plan for the future. Interviews were carried out with 39 older adults ( M age = 81 years) who were experiencing disability and illness but who lived in their own home. Guiding questions for the interview focused on present living circumstances, but for the present qualitative analysis, all references to the future, and to future residence changes, were extracted. This approach allowed us to observe how older adults spontaneously address issues of future planning when not constrained to do so. Results supported the use of a positivity bias, as well as a risk-aversive decision-making style. These older adults seemed to be prioritizing present emotional well-being by avoiding thoughts of future risks and thereby eschewing proactive coping.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.103
GPT teacher head0.424
Teacher spread0.321 · 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
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

Citations19
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied GerontologySame topicAging and Gerontology ResearchFrench-language works237,207