Work–family interference and work-related stress among teachers at Unidad Educativa Jaime Roldós Aguilera - Santo Domingo, 2026

Abstract

Introduction: Work–family interference involves reconciliation demands that should be examined alongside work organisation and teachers’ recovery. The objective was to analyse the relationship between the available indicators of work–family interference and work-related stress among teachers at Unidad Educativa Jaime Roldós Aguilera, Santo Domingo, in 2026, within the context of psychosocial work dimensions. Materials and methods: A quantitative, nonexperimental, cross-sectional, descriptive-correlational study was conducted with a nonprobability convenience sample of 34 morning-shift teachers from block 1. The Ministry of Labour’s 58-item questionnaire was used. Chart distributions, based on 24, 25 or 34 responses depending on the item, were compiled by dimension as response frequencies, without estimating individual prevalence. Separately, 34 paired scores for items 3 and 46 were reanalysed using Spearman’s and Pearson’s correlations. Results: Disagreement or low agreement accounted for 29.2% of responses regarding workload and pace, 26.5% regarding recovery and 22.1% regarding work–family interference. Within the latter component, the opportunity to address family problems received 10 lower-agreement responses among 34 teachers. The association between the two indicators was weak and positive, without statistical significance: rho = 0.206; p = 0.241; Pearson’s r = 0.216; p = 0.220. Measures for collective workload planning, reconciliation and recovery were designed, including monitoring indicators. Discussion: The findings support an organisational approach to prevention, but discrepancies between charts and individual records, together with single-item measurement, limit interpretation of the association. Conclusions: Local preventive needs were identified; causality, clinical stress levels and the effectiveness of the proposed measures were not established. Further assessment requires a complete individual-level dataset and specific measures of both constructs.