A framework analysis of free-text data from the neonatal survey 2014

Ainley, Esther; King, Jenny; Käsbauer, Susanne; Cooper, Robyn

doi:10.1016/j.jnn.2017.09.003

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…The framework approach is considered appropriate for applied and practice-relevant research, such as this study, given that it encourages themes to emerge inductively as well as being deductively derived from the study aims [53,54]. It is also a fruitful approach for the qualitative analysis of open-ended survey responses [55,56,57]. Initial coding of a number of responses into themes was conducted by the first two authors.…”

Section: Methodsmentioning

confidence: 99%

How Do Nurses Cope with Shift Work? A Qualitative Analysis of Open-Ended Responses from a Survey of Nurses

Savic

Ogeil

Sechtig

et al. 2019

IJERPH

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Nurses are frequently required to engage in shift work given the 24/7 nature of modern healthcare provision. Despite the health and wellbeing costs associated with shift work, little is known about the types of coping strategies employed by nurses. It may be important for nurses to adopt strategies to cope with shift work in order to prevent burnout, maintain wellbeing, and ensure high quality care to patients. This paper explores common strategies employed by nurses to cope with shift work. A workforce survey was completed by 449 shift working nurses that were recruited from a major metropolitan health service in Melbourne, Australia. Responses to open-ended questions about coping strategies were analysed using the framework approach to thematic analysis. Four interconnected main themes emerged from the data: (i) health practices, (ii) social and leisure, (iii) cognitive coping strategies, and (iv) work-related coping strategies. Although a range of coping strategies were identified, sleep difficulties often hindered the effective use of coping strategies, potentially exacerbating poor health outcomes. Findings suggest that in addition to improving nurses’ abilities to employ effective coping strategies on an individual level, workplaces also play an important role in facilitating nurses’ wellbeing.

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Section: Methodsmentioning

confidence: 99%

How Do Nurses Cope with Shift Work? A Qualitative Analysis of Open-Ended Responses from a Survey of Nurses

Savic

Ogeil

Sechtig

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…Most responses (83.6%) to the two questions were categorized into oneword topics. A difference with previous work [5][6][7][8][9][10][11][12] is using unsupervised topic modelling rather than a supervised approach. An advantage of an unsupervised approach is that the topics are an exact representation of the patients' feedback, without adding interpretation to the data.…”

Section: Automatically Defining Priorities For Improvementmentioning

confidence: 97%

“…NLP can discover 'topics' occurring in a collection of documents [4] (i.e., topic modelling). Topic modelling was previously applied to categorize patient experience free-text responses into predefined topics [5][6][7][8][9][10][11][12]. These studies used an supervised approach [3] meaning topic names were chosen in advance by the authors (i.e.…”

Section: Introductionmentioning

confidence: 99%

How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach

Cammel

Vos

Soest

et al. 2020

BMC Med Inform Decis Mak

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Background Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement. Methods This retrospective study used routinely collected patient experience data from two hospitals. A data-driven NLP approach was used. Free-text responses were categorized into topics, subtopics (i.e. n-grams) and labelled with a sentiment score. The indicator ‘impact’, combining sentiment and frequency, was calculated to reveal topics to improve, monitor or celebrate. The topic modelling architecture was tested on data from a second hospital to examine whether the architecture is transferable to another hospital. Results A total of 38,664 survey responses from the first hospital resulted in 127 topics and 294 n-grams. The indicator ‘impact’ revealed n-grams to celebrate (15.3%), improve (8.8%), and monitor (16.7%). For hospital 2, a similar percentage of free-text responses could be labelled with a topic and n-grams. Between-hospitals, most topics (69.7%) were similar, but 32.2% of topics for hospital 1 and 29.0% of topics for hospital 2 were unique. Conclusions In both hospitals, NLP techniques could be used to categorize patient experience free-text responses into topics, sentiment labels and to define priorities for improvement. The model’s architecture was shown to be hospital-specific as it was able to discover new topics for the second hospital. These methods should be considered for future patient experience analyses to make better use of this valuable source of information.

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“…lar studies analysing open-ended survey responses have utilised the framework approach productively. 41,42 This involved the authors R.…”

Section: Qualitative Analysismentioning

confidence: 99%

Assembling a ‘good’ and ‘bad’ night's sleep: A multifactorial proposition

et al. 2021

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Introduction:There is increasing demand for effective sleep health and promotion strategies that are accessible to the general population. Sleep hygiene has traditionally focused on promoting behaviours that facilitate good sleep and avoiding behaviours that interfere with sleep. However, simply following good sleep hygiene practices has proven largely ineffective in clinical samples suffering insomnia, and effectiveness of these recommendations in the general population has also been questioned. One reason for this may be the lack of consideration with respect to interactions between these different factors, as many elements determine a 'good' and a 'bad' night's sleep. Methods:The present paper utilised a mixed methods approach using a combination of framework approach thematic analysis (qualitative) and the number of mentions of different characteristics defining a good and bad night's sleep (quantitative) in participants' (N = 282 (157 females), mean age 27 years) accounts of their last good and bad night's sleep, and how (if at all) alcohol or drugs played a role. Results:We found that both good and bad night's sleep were influenced by multiple factors and that often the same factors were implicated in both. A significant number of participants described elements of the sleep episode (e.g., timing pre-vs. postmidnight, consistency of routine, sleep onset latency, sleep environment), or other behavioural or environmental factors (e.g., alcohol or drug use, mood, food consumed prior to sleep) they perceived were involved in their experience of a good or bad night's sleep.Conclusions: Rather than isolating factors as is typically done in sleep hygiene principles, or viewing these factors as discrete and/or having predictable effects on sleep, it is important to examine how these factors coalesce to shape a good and bad night's sleep.

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A framework analysis of free-text data from the neonatal survey 2014

Cited by 5 publications

References 21 publications

How Do Nurses Cope with Shift Work? A Qualitative Analysis of Open-Ended Responses from a Survey of Nurses

How Do Nurses Cope with Shift Work? A Qualitative Analysis of Open-Ended Responses from a Survey of Nurses

How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach

Assembling a ‘good’ and ‘bad’ night's sleep: A multifactorial proposition

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