ObjectivesTo explore how former breast cancer patients (BCPs) experience the daily-life impact of radiotherapy (RT) side effects and to examine how these narratives can inform the selection of patient stories for the BRASA patient decision aid (BRASA-PtDA).MethodsSemi-structured interviews were conducted with fourteen former BCPs who completed adjuvant RT 6 months–5 years earlier. Thematic analysis identified locoregional, general, and overarching impacts on quality of life (QoL). Narrative excerpts were classified by type, purpose, and evaluative valence.ResultsParticipants described locoregional side effects (pain, reduced arm function, changes in breast appearance, skin irritation) and general effects (fatigue, concentration difficulties) affecting daily activities and self-perception. Three overarching themes—comparisons to the pre-treatment self, acceptance, and work—captured broader QoL impacts. All narratives were experiential or outcome-focused, mostly negative in valence. Thirteen balanced narratives were selected for potential inclusion in the BRASA-PtDA.ConclusionsNarratives from BCPs illustrate how RT side effects shape daily life and overall QoL. Structured narrative classification provided a transparent method for selecting stories for a decision aid. Future research should examine how different narrative types influence patient engagement and decision outcomes.InnovationTo our knowledge, this is the first qualitative study to examine how BCPs live with RT side effects, highlighting impacts on daily life that are under-communicated in current practice in the Netherlands and internationally. Findings provide rich insights for improving risk communication and shared decision making by moving beyond symptom-focused information to incorporate patients' lived experiences.

AimDuring objective structured clinical examinations (OSCEs), perceived empathy can be rated by medical students, simulated patients (SPs) or external observers. This study aimed to examine how different ratings of medical students' perceived empathy correlate with each other, as well as with measures of behavioral empathy using a standardized coding system.MethodsVoluntary medical students who participated to a formative OSCE self-rated their empathy using a global empathy score. SPs and an external observer rated students' perceived empathy using the Jefferson Scale of Patient's Perceptions of Physician Empathy (JSPPPE-Patient; JSPPE-Observer) and global scores. The Verona Coding System (VR-CoDES) captured patients' emotions and students' responses (verbal empathy). Non-verbal behavior and five sub-behaviors were assessed via an ordinal scale (1–5). Associations were calculated using correlation analyses.Results56 medical students participated. There was no significant association between students' self-perceived empathy and other measures of perceived empathy. Global SPs' and observer's measures of perceived empathy as well as JSPPPE scales correlated with verbal empathy (VR-CoDES) (global ρ =0.27 p < .05; respectively 0.56, p < .01), JSPPPE-P (ρ = 0.34, p < .05), and JSPPPE-O (ρ = 0.41, p < .01). Non-verbal behavior correlated with global scores (SP ρ = 0.39 p < .01; observer ρ = 0.66, p < .01) and verbal empathy (VR-CoDES) (ρ = 0.38, p < .01).ConclusionSPs and observers' ratings of perceived empathy using global scores as well as measurement of non-verbal behavior are valid ways to assess medical students' behavioral empathy.InnovationStandardized coding systems are useful to assess which measures of medical students' perceived empathy used in OSCEs best correlate with behavioral empathy.

To evaluate ChatGPT's accuracy as information source for women and maternity-care workers on "nutrition" and "red flags" in pregnancy. Accuracy of ChatGPT-generated recommendations was assessed by a 5-point Likert scale by eight raters for ten indicators per topic in four languages (French, English, German and Dutch). Accuracy and interrater agreement were calculated per topic and language. For both topics, median accuracy scores of ChatGPT-generated recommendations were excellent (5.0; IQR 4-5) independently of language. Median accuracy scores varied with a maximum of 1 on a 5-point Likert-scare according to question's framing. Overall accuracy scores were 83-89% for 'nutrition in pregnancy' versus 96-98% for 'red flags in pregnancy'. Inter-rater agreement was good to excellent for both topics. Although ChatGPT generated accurate recommendations regarding the tested indicators for nutrition and red flags during pregnancy, women should be aware of ChatGPT's limitations such as inconsistencies according to formulation, language and the woman's personal context. Despite a growing interest in the potential use of artificial intelligence in healthcare, this is, to the best of our knowledge, the first study assessing potential limitations that may impact accuracy of ChatGPT-generated recommendations such as language and question-framing in key domains of perinatal health.