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Chinese Journal of Practical Nursing ; (36): 2019-2027, 2022.
Article in Chinese | WPRIM | ID: wpr-954965

ABSTRACT

Objective:To construct a discharge preparation service program for elderly lung cancer patients and their caregivers based on binary coping theory, evaluate its clinical feasibility.Methods:To develop discharge preparation service for elderly lung cancer patients and their caregivers by literature retrieval, expert correspondence consultation and theoretical research. The program included three themes: binary evaluation, binary management behaviorand binary health, and six intervention strategies: evaluation of symptoms, care goals and intentions, decision making, emotional communication support, management of physical function changes and general health behavior. From July to September 2021, 30 pairs of elderly lung cancer patients and their caregivers were recruited and assigned into the control group and the experimental group with 15 pairs in each group according to random numbers generated by computer. The feasibility and clinical effect of the program were evaluated by feasibility evaluation index, discharge readiness, dual coping and comprehensive needs scale of caregivers.Results:In this study, the recruitment rate was 76%(32/42), the retention rate was 94%(30/32), and the acceptance rate of the intervention plan in the experimental group was 100%. After intervention, the total scores of discharge readiness was (83.00 ± 2.59), the patient dyadic coping was (136.80 ± 4.54), caregiver dyadic coping was (136.33 ± 3.56) and caregiver comprehensive needs was (124.37 ± 11.69) in the experimental group, which were better than those of in the control group, (74.40 ± 4.17), (129.07 ± 4.83), (120.33 ± 9.17) and (206.57 ± 9.14), the differences were statistically significant ( t values were -6.78- 5.54, all P< 0.05). Conclusion:The discharge preparation service plan for elderly lung cancer patients and caregivers based on dyadic coping theory is scientific and feasible, but the plan still needs further optimization, improvement and large sample randomized controlled trial to verify.

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