Dataset Information

Professional issues in maternal mental health scale (PIMMHS): The development and initial validation of a brief and valid measure.

ABSTRACT:

Introduction

The life-threatening consequences of perinatal mental health problems (PMHP) are well documented. Midwives are ideally placed to effectively identify women at risk and facilitate early intervention. However, a multitude of factors contribute to failure in recognition and treatment. It would be of value for service providers to be able to identify key professional issues in their own context. The present study sought to develop and evaluate a 'professional issues in maternal mental health' scale (PIMMHS), explore its psychometric properties and potential application.

Methods

A cross-sectional design and instrument evaluation approach was taken to investigate the psychometric properties of the PIMMHS. A total of 266 student midwives from 10 UK institutions completed the PIMMHS via Survey Monkey.

Results

PIMMHS comprises two sub-scales of emotion/communication (PIMMHSEmotion sub-scale) and training (PIMMHS-Training sub-scale). Both PIMMHS subscales demonstrate adequate divergent and convergent validity. Sub-optimal internal consistency was observed for the training sub-scale, however, the PIMMHS-Training had a more impressive effect size in terms of known-groups discriminant validity compared to PIMMHS-Emotion.

Conclusions

The PIMMHS appears to be a sound psychometric instrument for assessing professional issues that influence the practice of student midwives in PMH. The PIMMHS could support education providers to identify areas for curriculum development, as well as maternity services in proactive assessment of service provision, to identify training and service development opportunities.

SUBMITTER: Jomeen J

PROVIDER: S-EPMC7846036 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Professional issues in maternal mental health scale (PIMMHS): The development and initial validation of a brief and valid measure.

Jomeen Julie J Jarrett Patricia P Martin Colin R CR

European journal of midwifery 20180205

<h4>Introduction</h4>The life-threatening consequences of perinatal mental health problems (PMHP) are well documented. Midwives are ideally placed to effectively identify women at risk and facilitate early intervention. However, a multitude of factors contribute to failure in recognition and treatment. It would be of value for service providers to be able to identify key professional issues in their own context. The present study sought to develop and evaluate a 'professional issues in maternal ...[more]

PMID: 33537563

Similar Datasets

Project description:The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals' beliefs about AI's influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale's structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users' general attitudes toward AI.

Dataset Information

Professional issues in maternal mental health scale (PIMMHS): The development and initial validation of a brief and valid measure.

Introduction

Methods

Results

Conclusions

Publications

Professional issues in maternal mental health scale (PIMMHS): The development and initial validation of a brief and valid measure.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets