Conectar
To identify a correlation between unmet needs and HL levels in people with Multiple Sclerosis (pwMS) and to evaluate how sociodemographic characteristics influence HL levels and unmet needs.
A cross-sectional study.
The study was conducted using a questionnaire including the HLS19-Q12 to assess HL and the Long-term Unmet Needs in Multiple Sclerosis tool, which evaluates five domains (neuropsychological, ambulation, physical, interpersonal relationship, and informational) and identifies whether needs are met or unmet and the desire for support.
Among the 116 participants included in the study, the overall HL level was sufficient. Mean scores across unmet needs domains were comparable. A significant difference in HL emerged in the informational domain, where participants reporting informational needs and a desire for support showed higher mean ranks. Although not significant, participants who acknowledged a need and expressed a desire for help showed higher mean ranks in HLS19-Q12 scores across several domains. No significant correlations were found between HL and unmet needs domains.
HL levels may enhance patients' ability to recognize and express needs without necessarily ensuring that these needs are met.
Routine assessment of both HL and unmet needs may help healthcare professionals identify patients who recognize problems but lack the structural support to address them.
The impact of HL on need recognition and communication, together with the complexity and interconnectedness of unmet needs, highlights the need for healthcare systems to implement organizational, systemic, and multidimensional interventions aimed at promoting HL and effectively addressing patients' needs. Such strategies may support better disease management and improve quality of life in pwMS.
This study was reported according to STROBE checklist.
None.
To assess healthcare professionals' digital health competence and its associated factors.
Cross-sectional study.
The study was conducted from October 2023 to April 2024 among healthcare professionals in Italy, using convenience and snowball sampling. The questionnaire included four sections assessing: (i) socio-demographic and work-related characteristics; (ii) use of digital solutions as part of work and in free time, and communication channels to counsel clients in work; and DigiHealthCom and DigiComInf instruments including measurements of (iii) digital health competence and (iv) managerial, organisational and collegiality factors. K-means cluster analysis was employed to identify clusters of digital health competence; descriptive statistics to summarise characteristics and ANOVA and Chi-square tests to assess cluster differences.
Among 301 healthcare professionals, the majority were nurses (n = 287, 95.3%). Three clusters were identified: cluster 1 showing the lowest, cluster 2 moderate and cluster 3 the highest digital health competence. Most participants (n = 193, 64.1%) belonged to cluster 3. Despite their proficiency, clusters 2 and 3 scored significantly lower on ethical competence. Least digitally competent professionals had significantly higher work experience, while the most competent reported stronger support from management, organisation, and colleagues. Communication channels for counselling clients and digital device use, both at work and during free time, were predominantly traditional technologies.
Educational programmes and organisational policies prioritising digital health competence development are needed to advance digital transition and equity in the healthcare workforce.
Greater emphasis should be placed on the ethical aspects, with interventions tailored to healthcare professionals' digital health competence. Training and policies involving managers and colleagues, such as mentoring and distributed leadership, could help bridge the digital divide. Alongside traditional devices, the adoption of advanced technologies should be promoted.
This study adheres to the STROBE checklist.
None.
FreshRSS 