Healthcare misinformation: Recognition and response in pre-licensure nursing education
Bibliographic record
Abstract
Background and objective: The amount of medical misinformation accessible to the public presents challenges for the healthcare community. Nursing graduates require knowledge, skills, and attitudes to enter practice prepared to recognize and respond to misinformation. The aim of the study was to assess the student nurse’s ability to recognize and respond to misinformation in the media.Methods: A total of 14 prelicensure students were recruited for a qualitative study involving watching/listening to birth-related media containing misinformation. Ability to recognize and respond to misinformation was evaluated using reflective journals guided by Tanner’s Clinical Judgment Model.Results: Evidence of components of clinical judgment were noted: noticing in 14/14; interpreting in 11/14; and responding in 8/14 journals. Further analysis yielded themes: media/social media misinformation can impact care that people seek; students recognize nursing responsibility to respond to misinformation; while most are able to recognize misinformation, many do not know how to respond.Conclusions: Students recognized misinformation, but the degree to which they were prepared to respond to it is unclear. Further study is needed to determine the ability of prelicensure students to respond to misinformation and to determine if programs should evaluate for incorporation of misinformation into curricula.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".