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Record W4312327925 · doi:10.4103/jiag.jiag_2_22

Adaptation Needs and Concerns of Senior Citizens Living in Old Age Homes

2022· article· en· W4312327925 on OpenAlexaboutno aff
Aleena Mathai, Sojan Antony, Perarul Sivakumar

Bibliographic record

VenueJournal of The Indian Academy of Geriatrics · 2022
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsNonprobability samplingAdaptation (eye)GerontologyContext (archaeology)Activities of daily livingPsychologyNeeds assessmentQualitative researchMedicineSociologyGeographyPopulationEnvironmental healthSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

Context: Adaptation needs of senior citizens are not well explored in research, especially among people living in old age homes situated in developing countries like India. Aim: The aim of the study was to understand the needs and concerns of senior citizens living in old age homes and its effect on their adaptation. Subjects and Methods: Using the purposive sampling method, fifteen residents of an old age home and their three caregivers were interviewed to describe their views and experiences. The tools used were the Montreal Cognitive Assessment and an in-depth interview schedule. Statistical Analysis: Qualitative data were analyzed using Atlas ti. 7 for identifying major themes and subthemes. Results: The results showed that the major needs and concerns of senior citizens were unmet medical needs, difficulty to handle negative attitudes of staff, difficulty to adapt with new environment and culture, and emotional issues. Conclusion: Findings indicate the importance of addressing the adaptation needs of the elderly living in old age homes.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.046
GPT teacher head0.340
Teacher spread0.293 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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