The impact of the <scp>COVID</scp>‐19 pandemic on the well‐being of individuals with persistent postconcussive symptoms: A qualitative study
Bibliographic record
Abstract
BACKGROUND: In response to the COVID-19 pandemic, public health measures were implemented that closed essential businesses, mandated social distancing, and imposed substantial changes to the routine care experienced by patients with mild traumatic brain injury (mTBI) and persistent postconcussive symptoms (PPCS). Patients with PPCS often rely on a comprehensive care team, requiring in-person treatments and consistent care. Little information exists regarding how access to these services have been affected by public health measures and what outcome the measures have had on the recovery of patients with PPCS. OBJECTIVE: To explore the impact of the restriction of in-person treatments, shifts to virtual care, and global public health measures on the recovery and psychological well-being of patients with PPCS. DESIGN: Qualitative interviews were recorded, transcribed, and analyzed using a reflexive thematic analysis approach to identify the main impacts of the public health measures on participants with PPCS. SETTING: Participant interviews were completed remotely via telephone or video-calling software during province-wide shutdowns. PARTICIPANTS: 20 individuals with PPCS who attended the institution's Brain Injury Program consented to participate. INTERVENTIONS: Not applicable. RESULTS: The impacts of the public health measures emerged most prominently in three main categories: (1) day-to-day lived experiences, (2) personal health status, and (3) health service experiences and barriers. CONCLUSIONS: This in-depth investigation of the lived experiences of patients with PPCS outlines how the COVID-19 public health measures negatively affected their care and well-being. The analysis identified that through increasing social support systems, providing better access to standard or remote treatment, and developing more effective telehealth strategies, this population could be better supported in the event of future public health measures.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".