ArticleBMC nursing2026
Daily temporal associations among AI replacement anxiety, state anxiety, and academic burnout indicators in medicine and nursing students: a 10-day diary study.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) is increasingly entering medicine and nursing education, raising concerns about how healthcare students respond to AI-related occupational uncertainty. AI replacement anxiety (ARA) may become more or less salient across academic days, but little is known about its short-term within-person temporal associations with state anxiety and day-level academic burnout indicators.
methodsThis 10-day weekday diary study included 751 medicine and nursing students who provided 4,625 observed diary records after quality screening. Daily surveys assessed ARA and three academic burnout constructs using single day-adapted proxy indicators, together with a two-item state-anxiety measure. Two-level Bayesian dynamic structural equation modeling tested six primary dimension-specific models in the ARA-to-state-anxiety and state-anxiety-to-ARA directions. Supplementary joint multivariate models, a stricter 695-participant sensitivity sample, and a targeted calendar-spacing sensitivity analysis examined unique effects, posterior contrasts, and robustness.
resultsIn the ARA-to-state-anxiety direction, higher daily ARA was associated with higher subsequent cynicism, and higher cynicism was associated with later state anxiety. This M2 component pattern remained supported when the weekend interval was represented using calendar-day spacing. In the reverse direction, higher state anxiety was consistently associated with a higher subsequent score on the academic-inefficacy proxy indicator, but the subsequent association of this indicator with ARA was less robust across model specifications and was attenuated in the weekend-spacing sensitivity analysis. The model-based indirect effects remained uncertain, with 95% credible intervals including zero. Joint models indicated that the clearest cross-dimension differences were concentrated mainly in first-stage associations rather than complete unique downstream indirect processes.
conclusionsThe findings support dimension-specific temporal associations among ARA, state anxiety, and academic burnout-related daily indicators, highlighting cynicism and academic-inefficacy-related daily appraisals as particularly relevant components of the temporal links between ARA and state anxiety among medicine and nursing students. These findings provide a more differentiated basis for future research on targeted support and intervention in health-professions education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.