Use of the FacebookTM social network in data collection and dissemination of evidence
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.309 | 0.440 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".