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Record W4283791238 · doi:10.33448/rsd-v11i9.31511

Perspectivas de pacientes com história de infarto do miocárdio e do enfermeiro na adequação de uma intervenção para adesão medicamentosa

2022· article· pt· W4283791238 on OpenAlexafffund
Rafaela Batista dos Santos Pedrosa, Maria Cecília Bueno Jayme Gallani, Andressa Teoli Nunciaroni, Mariana Dolce Marques, Karyne Duval, Nathalia Malaman Galhardi, Roberta Cunha Matheus Rodrigues

Bibliographic record

VenueResearch Society and Development · 2022
Typearticle
Languagept
FieldSocial Sciences
TopicPublic Health in Brazil
Canadian institutionsUniversité Laval
FundersUniversidade Estadual de CampinasUniversité Laval
KeywordsHumanitiesPsychologyPhysicsMedicinePhilosophy

Abstract

fetched live from OpenAlex

Este estudo identificou as condições que aumentam a viabilidade, aceitabilidade e potencial de eficácia de uma intervenção para promover a adesão medicamentosa. Trata-se de um estudo qualitativo, de delineamento transversal e a técnica de grupo focal foi utilizada com 15 pacientes e 14 enfermeiros para identificar a opinião sobre a intervenção para adesão aos medicamentos cardioprotetores na atenção primária à saúde. As sessões foram gravadas em áudio, transcritas na íntegra e discutidas detalhadamente para obter concordância entre os avaliadores. A lista de verificação COREQ do EQUATOR foi utilizada. Os participantes sugeriram que a intervenção fosse aplicada de forma escrita e verbal com duração aproximada de 30 minutos e reforço com intervalo de trinta dias. O esquecimento, a complexidade do regime terapêutico, a falta de acesso gratuito aos medicamentos, a compreensão limitada de sua doença e a escolha pessoal do paciente de não aderir, foram mencionados como barreiras à adesão. Os participantes ajustaram o modo de aplicação e a dose da intervenção.

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)0.004
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.192
GPT teacher head0.463
Teacher spread0.271 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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