Beyond Checklists: A Nursing Informatics Education Strategy for Undergraduate Nursing Students Appraising Health Information on Social Networking Sites (SNS)
Bibliographic record
Abstract
Increasingly internet social networking sites are used in healthcare to support, communicate and offer information platforms between healthcare providers, users, and the public. Undergraduate nursing students draw on various sources of evidence to inform best-practice decisions in collaboration with patients and the healthcare team. Student or patient-initiated access of information from social networking sites necessitates high levels of informatics literacy. While students may reveal adept social networking site navigation skills, their capacity to appraise and apply information from these sites to their nursing practice, in ways that demonstrate informatics competence, requires further exploration. The purpose of this education project was to describe how students’ informatics competence was enriched through the development and implementation of a Credibility, Argument, Purpose and Evidence guide, compared to a previously implemented checklist as part of a digital health assignment. The Constructivist Online Learning Environment Survey evaluated student-learning perceptions using the new guide as well as the previously utilized checklist. The developed guide improved students’ perceptions of their ability to appraise social networking sites. Results revealed an improvement in students’ appreciation of the significance of moving beyond the use of checklists when appraising and evaluating social networking sites. Educational institutions assume a prominent role as stakeholders in curriculum development, to equip nursing students with informatics skills to critically appraise and evaluate information from various social networking sites and technologies, alongside other health knowledge, for ethical evidence informed nursing practice.
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 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 | 0.067 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".