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. 2021 Feb 17;21(1):59.
doi: 10.1186/s12911-021-01396-y.

User-centered and theory-based design of a professional training program on shared decision-making with older adults living with neurocognitive disorders: a mixed-methods study

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User-centered and theory-based design of a professional training program on shared decision-making with older adults living with neurocognitive disorders: a mixed-methods study

Moulikatou Adouni Lawani et al. BMC Med Inform Decis Mak. .

Abstract

Background: We know little about the best approaches to design training for healthcare professionals. We thus studied how user-centered and theory-based design contribute to the development of a distance learning program for professionals, to increase their shared decision-making (SDM) with older adults living with neurocognitive disorders and their caregivers.

Methods: In this mixed-methods study, healthcare professionals who worked in family medicine clinics and homecare services evaluated a training program in a user-centered approach with several iterative phases of quantitative and qualitative evaluation, each followed by modifications. The program comprised an e-learning activity and five evidence summaries. A subsample assessed the e-learning activity during semi-structured think-aloud sessions. A second subsample assessed the evidence summaries they received by email. All participants completed a theory-based questionnaire to assess their intention to adopt SDM. Descriptive statistical analyses and qualitative thematic analyses were integrated at each round to prioritize training improvements with regard to the determinants most likely to influence participants' intention.

Results: Of 106 participants, 98 completed their evaluations of either the e-learning activity or evidence summary (93%). The professions most represented were physicians (60%) and nurses (15%). Professionals valued the e-learning component to gain knowledge on the theory and practice of SDM, and the evidence summaries to apply the knowledge gained through the e-learning activity to diverse clinical contexts. The iterative design process allowed addressing most weaknesses reported. Participants' intentions to adopt SDM and to use the summaries were high at baseline and remained positive as the rounds progressed. Attitude and social influence significantly influenced participants' intention to use the evidence summaries (P < 0.0001). Despite strong intention and the tailoring of tools to users, certain factors external to the training program can still influence the effective use of these tools and the adoption of SDM in practice.

Conclusions: A theory-based and user-centered design approach for continuing professional development interventions on SDM with older adults living with neurocognitive disorders and their caregivers appeared useful to identify the most important determinants of learners' intentions to use SDM in their practice, and validate our initial interpretations of learners' assessments during the subsequent evaluation round.

Keywords: Aging; Behaviour change technique; Continuing professional development; Curricular development; Dementia; Implementation; Intervention design; User experience.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Theoretical model framing the current research
Fig. 2
Fig. 2
User-centered design process to tailor the training program to user needs. DB #1 = Choosing a support option to decrease caregiver burden; DB #2 = Choosing a non-pharmacological treatment to manage agitation, aggression, or psychotic symptoms; DB #3 = Deciding whether or not to stop driving following diagnosis; DB #4 = Choosing an option to improve quality of life; DB #5 = Deciding whether or not to prepare a power of attorney; HCP = HCP; v = version
Fig. 3
Fig. 3
Description of participants’ samples in each of the three sub-studies. DB = decision box
Fig. 4
Fig. 4
HCPs’ experiences of the training program
Fig. 5
Fig. 5
Mean levels of participant intention and its determinants after their review of each decision box, at each round. Legend: DB = decision box; R = round; SD = standard deviation

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