Judgements of health and social care professionals on a child protection referral of an unborn baby: Factorial survey

Elhinney, Helena Mc; Taylor, Brian J.; Sinclair, Marlene

doi:10.1016/j.chiabu.2021.104978

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…On completion of phase one, the survey tool underwent several iterations before it was ready for use in data collection. Phases two and three were also guided by established survey pretesting checklists [25,26] as well as additional checks identified in the factorial survey literature [22,27,28]. The study received approval from the then host institution's Health Sciences Ethics Committee (Reference number: 190202).…”

Section: Rationale For Selecting a Factorial Survey-based Approachmentioning

confidence: 99%

Development of a factorial survey for use in an international study examining clinicians’ likelihood to support the decision to initiate invasive long-term ventilation for a child (the TechChild study)

Quirke

Alexander

Masterson

et al. 2022

BMC Med Res Methodol

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Background The decision to initiate invasive long-term ventilation for a child with complex medical needs can be extremely challenging. TechChild is a research programme that aims to explore the liminal space between initial consideration of such technology dependence and the final decision. This paper presents a best practice example of the development of a unique use of the factorial survey method to identify the main influencing factors in this critical juncture in a child’s care. Methods We developed a within-subjects design factorial survey. In phase 1 (design) we defined the survey goal (dependent variable, mode and sample). We defined and constructed the factors and factor levels (independent variables) using previous qualitative research and existing scientific literature. We further refined these factors based on expert feedback from expert clinicians and a statistician. In phase two (pretesting), we subjected the survey tool to several iterations (cognitive interviewing, face validity testing, statistical review, usability testing). In phase three (piloting) testing focused on feasibility testing with members of the target population (n = 18). Ethical approval was obtained from the then host institution’s Health Sciences Ethics Committee. Results Initial refinement of factors was guided by literature and interviews with clinicians and grouped into four broad categories: Clinical, Child and Family, Organisational, and Professional characteristics. Extensive iterative consultations with clinical and statistical experts, including analysis of cognitive interviews, identified best practice in terms of appropriate: inclusion and order of clinical content; cognitive load and number of factors; as well as language used to suit an international audience. The pilot study confirmed feasibility of the survey. The final survey comprised a 43-item online tool including two age-based sets of clinical vignettes, eight of which were randomly presented to each participant from a total vignette population of 480. Conclusions This paper clearly explains the processes involved in the development of a factorial survey for the online environment that is internationally appropriate, relevant, and useful to research an increasingly important subject in modern healthcare. This paper provides a framework for researchers to apply a factorial survey approach in wider health research, making this underutilised approach more accessible to a wider audience.

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Section: Rationale For Selecting a Factorial Survey-based Approachmentioning

confidence: 99%

Development of a factorial survey for use in an international study examining clinicians’ likelihood to support the decision to initiate invasive long-term ventilation for a child (the TechChild study)

Quirke

Alexander

Masterson

et al. 2022

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…(Taylor, 2012 ). As in health care, these statements are not meant to imply that there is an absolute deterministic causation but, rather, that the risk factor (causative factor) increases the probability of the consequence occurring in the population to some measurable and significant extent, and that there are theoretical and temporal reasons to draw conclusions on the direction of causation (Mc Elhinney et al, 2021 ). Probabilistic statements about social welfare harms are perhaps even more misunderstood than those relating to (probabilistic) ‘causes’ of illnesses (Garrow & Yeheskel, 2017 ).…”

Section: Challenges Of Risk Science In Social Welfarementioning

confidence: 99%