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Record W1822200816

A extrema vulnerabilidade na transição dos cuidados hospitalares para o domicílio: uma análise sobre determinantes sociais de saúde

2015· article· pt· W1822200816 on OpenAlexvenueno aff
Jorge Lopes da Costa, Maria Dulce Gonçalves

Bibliographic record

VenueService social · 2015
Typearticle
Languagept
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Social vulnerabilityGerontologyPopulationPsychologyDemographyWelfare economicsGeographyMedicineSociologyPsychological interventionPsychiatryEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Maintaining patients quality of life and well-being, involves a plan of care and of social support aimed at physical, cognitive and / or functional recovery. However, the transition from hospital to other the level of care should consider the multidimensionality of the individual, a paradigm that promotes health and considers different risk factors. This study had the purpose to identify patients vulnerability after hospitalization in CHLO, between 2009-2012, highlighting those who were in a situation of extreme vulnerability. Methods: Firstly we analyzed the 4965 social episodes that were evaluated by the Social Work Department of the three hospital units that compose CHLO. In a second stage, we studied only the 1509 patients in extreme vulnerability. Results: The results showed four clusters of vulnerability with low income and with low literacy skills. Two groups were distinguished: One comprised a young, single and unemployed population, and the other an aged, married/widow and retired/or pensioner population. Extreme vulnerability affects olderpeople, particularly those with poor yields, excessive expenses or unable to manage resources. Most of these patients are unaware of their rights and lacks support from their families or proximity formal institutions. Conclusion: Data showed that extreme vulnerability is related to a population at risk whose individual, social and economic conditions are fragile and can jeopardize their wellfare.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0010.001

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 head0.093
GPT teacher head0.405
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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