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Record W2313357327 · doi:10.5505/agri.2014.41103

Pain Management of Elderly in Nursing Homes

2014· article· en· W2313357327 on OpenAlexfundaboutno aff
Filiz Özel, Yasemin Yıldırım, Çiçek Fadıloğlu

Bibliographic record

VenueAğrı - The Journal of The Turkish Society of Algology · 2014
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
FundersMcGill University
KeywordsPain managementMedicineElderly peoplePain scalePhysical therapyScale (ratio)Nursing homesNursingGerontology

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to determine pain management status of the elderly in nursing homes. METHODS: The descriptive study sample included 82 elderly who presented to two nursing homes in İzmir between February-July 2012. In this study, Elderly Identification Form, Mini-Mental Scale, McGill Pain Scale (MAS) and Pain Management Inventory were used as the data collection tool. RESULTS: It was determined that the highest rates of complaints the elderly individuals had were knee pain (64.6%) and headache (58.5%) in this study. Of the elderly people participating in the study, 96.3% took pain relievers for pain management and according to their statements, of the pain management methods they used, resting and directing attention to something else (X=5.76±0.87) and taking prescribed pain relievers (X=5.69±0.87) were very beneficial. CONCLUSION: In elderly individuals, it is important to use pharmacological and non-pharmacological methods for pain management. Therefore, it is recommended to determine the most frequently used methods for pain management by the elderly and to integrate them into the care plan.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.265
Teacher spread0.256 · 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 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

Citations19
Published2014
Admission routes2
Has abstractyes

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