Investigating the effect of using the Newman's systems model on the level of stress and the needs of depressed patients

Determining the Effect of Applying the Neuman Systems Model on Stress Levels and Needs of Patients with Depression

A randomized controlled clinical trial with two parallel groups (intervention and control), involving 80 participants, with blinding at the statistical analyst level. Convenience sampling will be used, and participants will be allocated to the two groups using block randomization and sealed, opaque, numbered envelopes.

This randomized controlled clinical trial involving 80 patients will be conducted at counseling centers in Zanjan, Iran, using block randomization for allocation to two parallel groups and blinding of the statistical analyst through anonymous coding.

In this study, the inclusion criteria are as follows: 1) A clinical diagnosis of depression based on DSM-5 or ICD-10 (regardless of the specific subtype). 2) Patients must be between 18 and 65 years of age. 3) Clinical stability (defined as no psychiatric hospitalizations and the maintenance of social functioning) and no use of psychiatric medications for the 3 months prior to the start of the study. 4) Provision of informed consent and explicit agreement by the patient to participate in the study. 5) Absence of other psychiatric disorders. The exclusion criteria are as follows: 1) The patient is unwilling to continue participation for any reason. 2) The patient experiences serious psychological or physical adverse effects. 3) The patient fails to adhere to the study’s recommendations and protocols. 4) The occurrence of an emergency or death that renders the continuation of the study impossible.

Intervention group: Receives nursing care based on the Neuman Systems Model in addition to routine care. Control group: Receives routine care provided at the healthcare center.

Stress levels; basic needs of patients with depression; depression severity.

Considering the study conditions and the need for rapid and practical access to the target population, convenience sampling was employed. In this method, individuals who met the inclusion criteria and were accessible to the researcher were selected. Accordingly, after identifying eligible participants, they were randomly allocated to the intervention and control groups using a block randomization technique, taking into account individual characteristics (gender, age, marital status, and educational level) to reduce bias and ensure balance between the groups. To implement random allocation, standard procedures were used, including sealed, coded envelopes and a pre-generated randomization list. Each allocation was placed in an opaque, numbered envelope. The researcher, without knowledge of the envelope's contents, opened the envelopes sequentially according to their numbers and assigned each participant to the designated group. Furthermore, the following formula was used to estimate the sample size as an alternative to Cochran's formula, which is commonly applied to small and finite populations: In this formula, $N$ represents the sample size for an effectively infinite population, which is generally assumed to be 384; $t$ is the coefficient corresponding to the selected confidence level; $S$ represents the estimated existing or preliminary variance; and $d$ denotes the acceptable margin of error. In statistical and research methodology references, this formula is commonly referred to as a modified sample size formula or a formula suitable for small samples. It is also known by other terms, such as the “sample size formula based on population variance for a finite sample” or the “sample size formula for a finite population.” Based on this formula, with a 95% confidence level, a 5% margin of error, a statistical power of 80%, and a variance of 1.96, the calculated sample size for a population of 384 was 80 participants.

Given the nature of the intervention and the way the research program was implemented, complete blinding of participants and researchers was not feasible. However, to reduce bias in the analysis of the results, blinding was implemented at the level of the statistical analyst. Specifically, data related to participants were provided to the analyst using anonymous codes, without identifying group allocation (intervention or control). Therefore, the statistical analyst was unaware of participants’ group assignments, and data analysis was performed without knowledge of the type of intervention received. This approach helps reduce observer bias during the data analysis phase.