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Record W3037354405

Hearing the Voice of the Resident in Long-Term Care Facilities-An Internationally Based Approach to Assessing Quality of Life

2018· article· en· W3037354405 on OpenAlexaffabout
John N. Morris, Anja Declercq, John P. Hirdes, Harriet Finne‐Soveri, Brant E. Fries, Mary James, Leon Geffen, Vahe Kehyayan, Kai Saks, Katarzyna Szczerbińska, Eva Topinková

Bibliographic record

VenueSTM:n Hallinnonalan avoin julkaisuarkisto (Julkari) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMinimum Data SetAutonomyLong-term careQuality of life (healthcare)GerontologyPsychologyQuality (philosophy)CzechMedicineNursing homesNursingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Abstract Objectives interRAI launched this study to introduce a set of standardized self-report measures through which residents of long-term care facilities (LTCFs) could describe their quality of life and services. This article reports on the international development effort, describing measures relative to privacy, food, security, comfort, autonomy, respect, staff responsiveness, relationships with staff, friendships, and activities. First, we evaluated these items individually and then combined them in summary scales. Second, we examined how the summary scales related to whether the residents did or did not say that the LTCFs in which they lived felt like home. Design Cross-sectional self-report surveys by residents of LTCFs regarding their quality of life and services. Setting/Participants Resident self-report data came from 16,017 individuals who resided in 355 LTCFs. Of this total, 7113 were from the Flanders region of Belgium, 5143 residents were from Canada, and 3358 residents were from the eastern and mid-western United States. Smaller data sets were collected from facilities in Australia (20), the Czech Republic (72), Estonia (103), Poland (118), and South Africa (87). Measurements The interRAI Self-Report Quality of Life Survey for LTCFs was used to assess residents' quality of life and services. It includes 49 items. Each area of inquiry (eg, autonomy) is represented by multiple items; the item sets have been designed to elicit resident responses that could range from highly positive to highly negative. Each item has a 5-item response set that ranges from “never” to “always.” Results Typically, we scored individual items scored based on the 2 most positive categories: “sometimes” and “always.” When these 2 categories were aggregated, among the more positive items were: being alone when wished (83%); decide what clothes to wear (85%); get needed services (87%); and treated with dignity by staff (88%). Areas with a less positive response included: staff knows resident's life story (30%); resident has enjoyable things to do on weekends (32%); resident has people to do things with (33%); and resident has friendly conversation with staff (45%). We identified 5 reliable scales; these scales were positively associated with the resident statement that the LTCF felt like home. Finally, international score standards were established for the items and scales. Conclusions This study establishes a set of standardized, self-report items and scales with which to assess the quality of life and services for residents in LTCFs. The study also demonstrates that these scales are significantly related to resident perception of the home-like quality of the facilities.

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)0.013
metaresearch head score (Gemma)0.020
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.435
Teacher spread0.311 · 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
Published2018
Admission routes2
Has abstractyes

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