Covid-19 threat and coping: application of protection motivation theory to the pandemic experiences of people affected by amyotrophic lateral sclerosis
Bibliographic record
Abstract
BACKGROUND: People with amyotrophic lateral sclerosis (ALS) are at high risk for severe outcomes from Covid-19 infection. Researchers exploring ALS and Covid-19 have focused primarily on system response and adaptation. Using Protection Motivation Theory, we investigated how people with ALS and family caregivers appraised and responded to Covid-19 threat, the 'costs' associated with pandemic response, and how health professionals and systems can better support people affected by ALS who are facing public health emergencies. METHODS: Data were drawn from the 'ALS Talk Project,' an asynchronous, moderated focus group study. Participants were recruited from regions across Canada. Seven groups met online over 14 weeks between January and July 2020. Fifty-three participants contributed to Covid-19 discussions. Data were qualitatively analyzed using directed content analysis and the constant-comparative approach. RESULTS: Participants learned about the Covid-19 pandemic from the media. They rapidly assessed their vulnerability and responded to Covid-19 threat by following recommendations from health authorities, information monitoring, and preparing for worst-case scenarios. Adopting protective behaviors had substantial response costs, including adaptations for medical care and home support workers, threatened access to advance care, and increased caregiver burden. Participants expressed need for ALS-specific, pandemic information from trusted health professionals and/or ALS health charities. Telemedicine introduced both conveniences and costs. Prior experience with ALS provided tools for coping with Covid-19. Threat and coping appraisal was a dynamic process involving ongoing vigilance and adaptation. Findings draw attention to the lack of emergency preparedness among participants and within health systems. CONCLUSIONS: Clinicians should engage ALS patients and families in ongoing discussions about pandemic coping, strategies to mitigate response costs, care pathways in the event of Covid-19 infection, and changing information about Covid-19 variants and vaccines. Healthcare systems should incorporate flexible approaches for medical care, leveraging the benefits of telemedicine and facilitating in-person interaction as needed and where possible. Research is needed to identify strategies to mitigate response costs and to further explore the interaction between prior experience and coping. Further study is also needed to determine how communication about emergency preparedness might be effectively incorporated into clinical care for those with ALS and other medically vulnerable populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".