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Record W2377690439 · doi:10.12927/hcpol.2016.24594

Effectiveness of Reablement: A Systematic Review

2016· review· fr· W2377690439 on OpenAlexaffvenue
Annie Tessier, Marie‐Dominique Beaulieu, Carrie Anna McGinn, Renée Latulippe

Bibliographic record

VenueHealthcare policy · 2016
Typereview
Languagefr
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité de MontréalInstitut National d'Excellence en Santé et en Services SociauxInstitute of Aging
Fundersnot available
KeywordsCINAHLPsycINFOMedicineRandomized controlled trialMEDLINEQuality of life (healthcare)GerontologyNursingPsychological interventionPolitical scienceSurgery

Abstract

fetched live from OpenAlex

The ageing of the population and the increasing need for long-term care services are global issues. Some countries have adapted homecare programs by introducing an intervention called reablement, which is aimed at optimizing independence. The effectiveness of reablement, as well as its different service models, was examined. A systematic literature review was conducted using MEDLINE, CINAHL, PsycINFO and EBM Reviews to search from 2001 to 2014. Core characteristics and facilitators of reablement implementation were identified from international experiences. Ten studies comprising a total of 14,742 participants (including four randomized trials, most of excellent or good quality) showed a positive impact of reablement, especially on health-related quality of life and service utilization. The implementation of reablement was studied in three regions, and all observed a reduction in healthcare service utilization. Considering its effectiveness and positive impact observed in several countries, the implementation of reablement is a promising avenue to be pursued by policy makers.

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)0.017
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.067
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.009
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.080
GPT teacher head0.487
Teacher spread0.406 · 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 designSystematic review
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

Citations103
Published2016
Admission routes2
Has abstractyes

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