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Record W4289109390 · doi:10.1111/jan.15395

Lessons from <scp>COVID</scp>‐19: Time for shared decision making in nursing practice

2022· article· en· W4289109390 on OpenAlexfundno aff
Kathryn S. Taylor, Melissa Hladek, Sabrina d Elias, Emerald Jenkins, Kim Robinson, Owen Smith, Sarah L. Szanton

Bibliographic record

VenueJournal of Advanced Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersInstitute of AgingNational Institute of Nursing ResearchNational Institute on AgingJohns Hopkins University
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)NursingPandemicMedicinePsychologyVirologyInternal medicineInfectious disease (medical specialty)

Abstract

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The COVID-19 pandemic revealed limitations in current approaches to educating the public and individual patients about their health, particularly in the context of misinformation on social media and other venues. Patients' existing belief systems influenced their perceptions about the validity of COVID-19 information and their subsequent decisions to receive a vaccine. In response, clinicians and researchers have reiterated the value of shared decision making to navigate patient preferences and ambiguity (Durand et al., 2022). Shared decision making is “an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options, to achieve informed preferences” (Elwyn et al., 2012). The evidence supporting shared decision making has grown since the term was introduced in the medical literature 40 years ago, but clinicians have not broadly translated it into practice. This period of reflection after rapid COVID-related changes to the structure and process of health care delivery presents an opportunity for nurses to lead shared decision making implementation to better align their professional role with the public health needs of society. In this commentary, we argue for an evolution of one of nursing's key functions—expanding the construct of patient education to include shared decision making—and suggest system-level changes that could enable the broad translation of shared decision making methods. We offer our perspectives as practising nurses, researchers, educators, and administrators across the health care continuum. Nurses can leverage patient education as part of a broader effort to establish therapeutic relationships, assess structural barriers to care and increase patients' self-efficacy. However, nurses are often taught that the goal of patient education is to improve patient adherence to evidence-based treatment; and in practice, patient education may be a simple knowledge transfer. For example, a nurse caring for an older patient or a patient with diabetes may educate them on the increased risk of severe COVID-19 outcomes and how to prevent infection. Despite best intentions, this approach does not align with the science of behaviour change or incorporate patients' expertise and may reinforce harmful power structures. Empirically valid behaviour change theory demonstrates that knowledge is necessary but not sufficient for improved health outcomes. A myriad of factors influences the pathway from knowledge to behaviour, including structural barriers, illness perception, goals, social relationships and self-efficacy (Bandura, 2004). Patient education alone rarely addresses them. In contrast to current practice, shared decision making would align patient education methods with both the evidence base for behaviour change and nursing's humanist values. Different shared decision making models exist, but key components across models include rapport-building and learning about the patient, their values and goals; creating or affirming the patient's awareness of choice; and describing treatment options (Bomhof-Roordink et al., 2019). By centring the person and their goals in the conversation, shared decision making allows for meaningful action planning that is context specific and builds authentic therapeutic relationships. Conceptual models and growing evidence link shared decision making and patient outcomes through a reduction in patients' anxiety and symptom burden and improvements in patient satisfaction and treatment adherence (Shay & Lafata, 2015). This reconceptualization of the nurse's role in patients' decision making would change the way nurses communicate with patients about COVID-19. They would invite patients to have a conversation about COVID-19 and build trust and rapport by being relatable—even vulnerable and sharing personal experiences. Nurses would engage in therapeutic listening, checking personal biases. This reduces the likelihood of prejudices, stereotypes, and discrimination introduced by personal beliefs, ignorance, or group memberships. They would offer information (e.g., about vaccines or masking) in the context of patients' goals. In so doing, nurses can help someone who is opposed to vaccines consider other public health measures like masking or social distancing. They can invite someone who is resistant to any measures to have another conversation in the future. Acceptance of health-promoting behaviours might not take place during the first conversation, but through relationship building and shared decision making nurses can help regain the trust that many patients have lost in the health care system. The message during COVID: Talk to a nurse about what you believe. Nurses care and can help you decide the best way to keep you and your loved ones healthy. Moreover, a person-centred approach to patient education and decision making will only become more needed as populations age and increase multimorbidity. As healthcare is currently structured, providers and specialists operate in silos. They possess technical expertise but may lack comfort or role clarity regarding behaviour change. Additionally, patients have access to an overwhelming amount of health information—both credible and misleading. Often they are left alone or with family members to interpret multiple or complex care plans, prioritize components of them and coordinate care. Nurses can practise shared decision making to transcend the noise and influence patients' health and behaviour using processes that centre patients' contexts, beliefs, values and preferences in individualized care planning. Once elicited, nurses can reiterate patient perspectives with specialists and other care team members to facilitate care plan integration. Nurses are well-positioned to engage in these simple but powerful conversations, yet broad shared decision making implementation requires system-level change. The current approach to patient education is reinforced in nursing curricula, electronic health records and healthcare regulations. The time pressures of real-world clinical practice limit opportunities for trust-building and deliberation. COVID-19 has only exacerbated them, and many nurses are disillusioned and weary. So how should nurses leverage professional strengths to move forward? Nursing faculty can integrate existing shared decision making competencies into nursing curricula and guide nursing students to practice shared decision making with time pressures during clinical rotations and via simulation. Chief nursing officers and other nursing leaders can identify champions at different organizational levels to implement shared decision making models and methods appropriate to their setting and patient populations. Health care organizations can pilot changes to the electronic health record that facilitate shared decision making. Rather than presenting checkboxes for patients' preferred learning style or barriers to learning, the electronic health record could encourage clinicians to ascertain patients' beliefs about their illness and goals for life at home. Other health information technology tools such as interactive decision aids, patient portals, personal health records and secure electronic messaging can help with shared decision making. For example, patients can access decision aids and relevant patient education materials via a patient portal and communicate with their health care team about the decision via secure messaging. Additionally, health care organizations will be incented to invest in technology infrastructure and human relationships as payment policy continues to shift from fee-for-service to fee-for-health and other population health approaches. Rather than using precious time to quickly skim multiple educational points, nurses can be encouraged through shared decision making to focus on what matters most to patients whilst developing connection with patients that brings meaning to work and improves patient ownership and outcomes. COVID-19 has demonstrated that individuals and communities who feel their autonomy and decision making power have been undermined may respond in a way that is detrimental to their well-being and the well-being of others. Historically, nurses have been at the forefront of efforts to promote patients' dignity, rights and autonomy. With practical system-level changes, nurses can continue to do this through shared decision making. All authors contributed to conceptualizing, writing, and editing the paper. Ms. Taylor led and coordinated the authorship team. Kathryn Taylor was supported by a grant from the National Institute of Nursing Research (F31NR109461). Melissa Hladek was supported by the Johns Hopkins University Older Americans Independence Center of the National Institute on Aging, award number P30AG021334. Sabrina Elias was supported by a grant from the National Institute of Nursing Research (FNR019523A). Emerald Jenkins was supported by a grant from the National Institute of Aging (R36AG07210–01). The authors have no conflicts of interest to report.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.154
GPT teacher head0.516
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes1
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