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Record W4401836024 · doi:10.51731/cjht.2024.957

Aging in Place

2024· article· en· W4401836024 on OpenAlexaboutno aff
CADTH

Bibliographic record

VenueCanadian Journal of Health Technologies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAging in placeGerontologyMedicine

Abstract

fetched live from OpenAlex

What Is the Issue? Canada is experiencing an important demographic transition toward an increasingly diverse aging population with an increased need for care and support during the later stages of life. Despite older adults’ strong preferences to live in their home or community for as long as possible, health systems are challenged to keep up with growing demands for home care, community care and support services to support conditions to age in place, when appropriate. What Did Canada’s Drug Agency Do? Canada’s Drug Agency prepared an Evidence Assessment report that identified and described the current context of aging and hinderances to aging in place in Canada, considerations relevant to aging in place for equity-deserving groups, strategies and initiatives intended to address unmet needs and improve outcomes, and systemic considerations related to implementing initiatives supporting aging in place. The Health Technology Expert Review Panel (HTERP) used the Canada’s Drug Agency Evidence Assessment report to inform deliberations and to develop objective, impartial, trusted pan-Canadian guidance for decision-makers when considering evidence-informed aging-in-place initiatives to support equitable aging in place in jurisdictions in Canada. What Is HTERP’s Position on Aging in Place? Everyone has the right to age with dignity. Aging is a normal part of life and may be accompanied by changes in health status and/or abilities to complete everyday self-care tasks. Older adults comprise a heterogenous population with a continuum of health care needs and risks, including people living with vitality, those with moderately complex care issues, and those with complex care issues. Health and social systems were originally designed to meet the less complex acute care needs of a younger population and operate largely in silos. They cannot adequately or proactively address the continuum of needs of many older adults and their caregivers. Aging in place is a dynamic and complex experience that occurs within a larger context that is not limited to health systems. Older adults’ autonomy and preferences for care are realized by the active role of informal unpaid caregivers. The population of older adults in Canada is diverse with varying needs, risks, and cultural norms and values related to aging in place. Barriers to aging in place may disproportionately affect members of equity-deserving groups that experience multiple and often intersecting historical, social, cultural, medical, structural, institutional, and environmental barriers to care and support. There must be equity in access to safe, appropriate, and ongoing care to support dignity in living in a place of one’s choosing for older adults as they age. What Is HTERP’s Guidance to Support Aging in Place? foster a system that prioritizes integrated models of care to address current and future unmet needs and bridge gaps in and between services identify interventions that are aligned within an integrated model of care and are responsive to the unique and complex needs of older adults and their unpaid caregivers ensure culturally appropriate and equitable opportunities for positive outcomes standardize core measures or indicators to guide data collection, analysis, use, and reporting that support robust evaluation and cross-jurisdictional comparisons of interventions.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.509
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0090.006
Scholarly communication0.0110.009
Open science0.0030.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0520.013

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 head0.038
GPT teacher head0.363
Teacher spread0.326 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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