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Record W4392757510 · doi:10.1080/00981389.2024.2324859

<i>Difficult but achievable</i> : medical social workers’ experiences transiting older adults from hospital care to nursing home in Nigeria

2024· article· en· W4392757510 on OpenAlexaff
Olanike Esther Oyebade, Oluwagbemiga Oyinlola, Cristina Asenjo Palma

Bibliographic record

VenueSocial Work in Health Care · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcGill University
Fundersnot available
KeywordsBureaucracyNursingSocial workHealth careQualitative researchPsychologyMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Very few literatures have focused on transition of older adults from hospitals to nursing homes in African region. As a first step, this study explored the experience of medical social worker when transiting older adult from the hospital to nursing home in southwestern region of Nigeria. A descriptive qualitative approach collected through a semi-structured interview among 16 medical social workers showed that there is limited availability of nursing home facilities in Nigeria. Additionally, bureaucratic and administrative hurdles often added to the complexities of facilitating seamless transitions into nursing care homes. Cultural beliefs and family dynamics exert a substantial influence on the decision-making process, making the task of medical social workers even more intricate. There is a need for a greater support from policymakers and healthcare authorities to address the challenges facing Nigerian medical social workers. Hence, to better understand and address these experiences, the healthcare system can better equip medical social workers to navigate the transitions effectively and ensure the well-being of older adults during this crucial phase of their lives is adequately supported.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.013
GPT teacher head0.367
Teacher spread0.354 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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