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Record W3133812354 · doi:10.2147/ppa.s297581

A Co-Design Process to Elaborate Educational Materials to Promote Appropriate Use of Antibiotics for Acute Lower Respiratory Tract Infections in Primary Healthcare in Catalonia (Spain)

2021· article· en· W3133812354 on OpenAlexaff
Laura Medina‐Perucha, Pablo Gálvez-Hernández, Ana García-Sangenís, Ana Moragas, Josep M. Cots, Anna Lanau-Roig, Alicia Espinosa Borrás, Isabel Pachón del Amo, Nieves Barragán, Ramon Monfà, Carl Llor, Anna Berenguera

Bibliographic record

VenuePatient Preference and Adherence · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsInstitute of Health Services and Policy ResearchUniversity of Toronto
FundersFundació la Marató de TV3
KeywordsMedicineFocus groupHealth carePromotion (chess)Target audienceNursingSociology

Abstract

fetched live from OpenAlex

PURPOSE: Co-design processes with patients allow developing health education materials, that are adapted to the population's knowledge and use of language, to reduce inappropriate antibiotic use. PATIENTS AND METHODS: This study presents a co-design process of educational material with patients (over 18 years old) with a previous diagnosis of acute lower respiratory tract infection. The co-design was framed within a qualitative study (Phase I, interviews; Phase II, focus group) conducted in Barcelona between April and September 2019. RESULTS: Twenty-nine semi-structured interviews were conducted. Six people participated in the focus group. Based on participants' narratives, educational materials can be useful to support healthcare consultations. Materials should be designed to be accessible in terms of the content and language used. CONCLUSION: The co-design of educational materials is essential for health promotion. This study presents an example of how materials can be co-developed with patients. The material elaborated in this study is being used for the ISAAC-CAT project and may be useful for future research, practice in health services and health policy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.314
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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