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Validação de “podcast” como meio de promoção de saúde na oncologia

2023· article· pt· W4320034524 on OpenAlexaff
Thaís Zilles Fritsch, Kimberly Bueno, Laura Helena Figueiredo da Silva, Taiane Freitas Saraiva, Uriel Pessano Husek Silva, Eliane Goldberg Rabin

Bibliographic record

VenueRevista Recien · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Uma das formas de levar conhecimento para pacientes com câncer e, consequentemente, melhorar a sua performance é agregar estratégias de cuidado envolvendo a comunicação entre profissional e paciente no sentido de educar e promover saúde por meio de tecnologias de fácil e rápido acesso. Sendo assim, objetivou-se validar o conteúdo do podcast como tecnologia de cuidado em saúde para pacientes oncológicos. Trata-se de um estudo aplicado na produção de mídia que se propõe a construir e desenvolver mídias de áudio (podcasts) que possam ser utilizadas no contexto assistencial. O estudo faz parte de um projeto maior dividido em três etapas distintas e complementares com 13 fases. Validou-se, por meio disso, o conteúdo, a configuração, a funcionalidade e a sonoridade do Podcast produzido por meio de juízes especialistas. Ressaltamos a importância da validação de podcasts os quais devem ser avaliados por especialistas antes de serem propagados para a comunidade em geral. Descritores: Estudos de Validação, Podcast, Educação em Saúde, Câncer. Validation of “podcast” as a means of health promoting in oncology Abstract: One of the ways to bring knowledge to cancer patients and, consequently, improve their performance is to add care strategies involving communication between professionals and patients to educate and promote health through easily and quickly accessible technologies. Therefore, the objective was to validate the podcast content as a healthcare technology for cancer patients. This is a study applied in the production of media that proposes to build and develop audio media (podcasts) that can be used in the care context. The study is part of a larger project divided into three distinct and complementary stages with 13 phases. Through this, the content, configuration, functionality, and sound of the Podcast produced by expert judges were validated. We emphasize the importance of validating podcasts that experts must evaluate before being propagated to the community. Descriptors: Validation Study, Podcast, Health Education, Cancer. Validación del “podcast” como medio de promoción de la salud en oncología Resumen: Una de las formas de acercar el conocimiento a los pacientes con cáncer y, consecuentemente, mejorar su desempeño es agregar estrategias de atención que involucren la comunicación entre profesionales y pacientes para educar y promover la salud a través de tecnologías de fácil y rápido acceso. Por tanto, el objetivo era validar el contenido del podcast como una tecnología sanitaria para pacientes oncológicos. Se trata de un estudio aplicado en la producción de medios que propone construir y desarrollar medios de audio (podcasts) que puedan ser utilizados en el contexto del cuidado. El estudio es parte de un proyecto mayor dividido en tres etapas distintas y complementarias con 13 fases. A través de este se validó el contenido, configuración, funcionalidad y sonido del Podcast producido por jueces expertos. Resaltamos la importancia de validar los podcasts que los expertos deben evaluar antes de ser propagados a la comunidad. Descriptores: Estudio de Validación, Podcast, Educación en Salud, Cancro.

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)0.027
metaresearch head score (Gemma)0.157
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.157
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.218
GPT teacher head0.480
Teacher spread0.262 · 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 designObservational
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".

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Citations2
Published2023
Admission routes1
Has abstractyes

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