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Record W2606836068 · doi:10.1590/1518-8345.1511.2870

Needs of family caregivers in home care for older adults

2017· article· en· W2606836068 on OpenAlexaff
Carla Cristiane Becker Kottwitz Bierhals, Naiana Oliveira dos Santos, F Fengler, Kamila Dellamora Raubustt, Dorothy Forbes, Lisiane Manganelli Girardi Paskulin

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNursing care and research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNormativeExploratory researchFamily caregiversNeeds assessmentPsychological interventionDescriptive statisticsNursingPsychologyQualitative researchContent analysisDescriptive researchInformation needsMedicineGerontologySociology

Abstract

fetched live from OpenAlex

Objective: to reveal the felt and normative needs of primary family caregivers when providing instrumental support to older adults enrolled in a Home Care Program in a Primary Health Service in the South of Brazil. Methods: using Bradshaw's taxonomy of needs to explore the caregiver's felt needs (stated needs) and normative needs (defined by professionals), a mixed exploratory study was conducted in three steps: Descriptive quantitative phase with 39 older adults and their caregiver, using a data sheet based on patient records; Qualitative exploratory phase that included 21 caregiver interviews, analyzed by content analysis; Systematic observation, using an observation guide with 16 caregivers, analyzed by descriptive statistics. Results: the felt needs were related to information about instrumental support activities and subjective aspects of care. Caregivers presented more normative needs related to medications care. Conclusion: understanding caregivers' needs allows nurses to plan interventions based on their particularities.

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)0.009
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
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.067
GPT teacher head0.446
Teacher spread0.379 · 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

Citations48
Published2017
Admission routes1
Has abstractyes

Explore more

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