Meeting the needs of families: facilitating access to credible healthcare information
Bibliographic record
Abstract
EBN engages readers through a range of online social media activities to debate issues important to nurses and nursing. EBN Opinion papers highlight and expand on these debates . Following an increase in the use of the internet in everyday life, research has identified that individuals are increasingly turning to the internet as a means for identifying information about healthcare conditions.1 One study identified that 98% of parents surveyed used the internet to search for information about their child's condition.2 While the use of the internet as an information seeking source is not problematic in itself, a substantial proportion of information has been identified as not being credible, meaning that families are often faced with poor quality non-evidence-based healthcare information.3 Compounding this problem is the fact that families typically do not have access to the traditional academic sources in which research studies are published, and there is a lengthy 17-year gap between publication of research findings and implementation of findings in clinical practice.4 In order to address these issues, a Twitter chat took place to explore how we can better reach families with evidence-based healthcare information. ### Working collaboratively with families There was overwhelming agreement among chat participants about the importance of ensuring that families are able …
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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.
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".