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Use of the FacebookTM social network in data collection and dissemination of evidence

2018· article· en· W2810557444 on OpenAlexaff
Ana Cláudia Vieira, Denise Harrison, Mariana Bueno, Natália Rocha Guimarães

Bibliographic record

VenueEscola Anna Nery · 2018
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsHospital for Sick ChildrenUniversity of Ottawa
Fundersnot available
KeywordsSnowball samplingData collectionPsychological interventionIntervention (counseling)PortugueseSocial mediaMedicineTabooDisseminationHealth professionalsPsychologyHealth careNursingComputer scienceWorld Wide WebSociology

Abstract

fetched live from OpenAlex

Abstract Aim: The aim of this study was to evaluate the use of the FacebookTM platform as a means of disseminating a video in Portuguese demonstrating the use of three interventions of pain management (breastfeeding, skin-to-skin contact, and sweet solutions) during minor procedures, and to evaluate prior knowledge, the range, dissemination and intent to use the strategies in the future. Method: This is a cross-sectional survey, which used the "virtual snowball" sampling method, aimed at parents and health professionals caring for neonates. The study was conducted in Brazil, through a FacebookTM page (https://www.facebook.com/sejadocecomosbebes), in which the video and a brief questionnaire were posted. Results: After three months the page reached 28,364 "views", in 45 municipalities across Brazil, 1531 people accessed the page, 709 responses to the questionnaires, 1126 "likes", and multiple positive comments. Almost all viewers (99.71%) answered they would use one of the pain reducing strategies. Conclusion: Our results indicate that using FacebookTM to deliver and evaluate an intervention is feasible, rapid in obtaining responses at a low cost, and it is promising for data collection and knowledge dissemination.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.162
GPT teacher head0.377
Teacher spread0.215 · 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 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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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