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PRIORITY NEEDS REFERRED BY FAMILIES OF RARE DISEASE PATIENTS

2016· article· en· W2559545768 on OpenAlexaff
Geisa dos Santos Luz, Mara Regina Santos da Silva, Francine deMontigny

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2016
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsSocial needsRare diseaseInclusion (mineral)DiseaseMedicineNursingHealth careSpecial needsFamily medicineGerontologyPsychologyPsychiatryPolitical sciencePathologySocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT Rare diseases cause strong impact in families and generate needs beyond those associated with the most frequent diseases. Some of these needs are the inclusion of new responsibilities and the relationship with the healthcare and social services. This study is aimed at identifying the priority needs of families of rare disease patients as perceived from the time of diagnosis. This is a qualitative study conducted with 16 relatives of rare disease patients who live in the state of Rio Grande do Sul. Data were collected from November 2012 to March 2013, through semi-structured interviews and submitted to content analysis, based on the bioecological system of human development. The results indicated the following priority needs: access to social and healthcare services; knowledge about rare diseases; social support structures; acceptance and social integration; preservation of personal and family life. It was concluded that (re)organizing services and meeting the specific needs are preconditions to qualify nursing care and soften the impact the rare disease has on the family.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.342
GPT teacher head0.478
Teacher spread0.136 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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