Improving Health and Well-Being of People With Post–COVID-19 Consequences in South Africa: Situation Analysis and Pilot Intervention Design
Bibliographic record
Abstract
Background: Multisystemic complications post-COVID-19 infection are increasingly described in the literature, yet guidance on the management remains limited. objectives: This study aimed to assess the needs, preferences, challenges, and existing interventions for individuals with post-COVID-19 symptoms. Based on this, we aimed to develop a context-adapted intervention to improve the overall health and well-being of individuals with post-COVID-19 complications. Methods: We conducted a cross-sectional mixed-methods situation analysis assessing the needs, preferences, challenges, and existing interventions for patients with post-COVID-19 symptoms. We collected data through questionnaires, semistructured in-depth interviews, and focus group discussions (FGDs) from individuals diagnosed with COVID-19 within the previous 18-month period and health care providers who managed patients with COVID-19 in both inpatient and outpatient settings. Quantitative data were summarized using descriptive statistics, qualitative data were transcribed, and deductive analysis focused on suggestions for future interventions. Findings guided the development of a group intervention. Results: We conducted 60 questionnaires, 13 interviews, and 3 FGDs. Questionnaires showed limited knowledge of post-COVID-19 complications at 26.7% (16/60). Of those who received any rehabilitation for COVID-19 (19/60, 31.7%), 94.7% (18/19) found it helpful for their recovery. Just over half (23/41, 56%) of those who did not receive rehabilitation reported that they would have liked to. The majority viewed rehabilitation as an important adjunct to post-COVID-19 care (56/60, 93.3%) and that support groups would be helpful (53/60, 88.3%). Qualitative results highlighted the need for mental health support, structured post-COVID-19 follow-up, and financial aid in post-COVID-19 care. Based on the insights from the situation analysis, the theory of change framework, and existing post-COVID-19 evidence, we designed and conducted a pilot support group and rehabilitation intervention for individuals with post-COVID-19 complications. Our main objective was to assess the change in physical and psychological well-being pre- and postintervention. The intervention included 8 weekly themed group sessions supplemented by home tasks. Effectiveness of the intervention was evaluated by questionnaires pre- and postintervention on post-COVID-19 symptoms, quality of life with the EuroQoL 5-Dimension 5-Level, short Warwick-Edinburgh Mental Wellbeing Scale, and physical function by spirometry and 1-minute sit-to-stand test. We also assessed the feasibility and acceptability of the intervention by questionnaires and semistructured in-depth interviews. The intervention outcome analysis is yet to be conducted. Conclusions: Insights from patients and health care providers on the characteristics of post-COVID-19 complications helped guide the development of a context-adapted intervention program with potential to improve health and well-being post-COVID-19.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".