An exploration into caring for a stroke-survivor in Lima, Peru: Emotional impact, stress factors, coping mechanisms and unmet needs of informal caregivers
Bibliographic record
Abstract
INTRODUCTION: Understanding local complexities and challenges of stroke-related caregiving are essential to develop appropriate interventions. Our study aimed to characterize the impact of post-stroke care among caregivers in a setting of transitioning economy. MATERIALS AND METHODS: Qualitative study based on in-depth interviews with primary caregivers of stroke survivors in Lima, Peru. Transcribed data was organized into the emotional impact of caregiving, main stress factors and coping mechanisms to deal with the caregiving role, as well as the unmet needs of caregivers. RESULTS: We interviewed twelve caregivers, mean age 52.5 years, 8/12 were females, who were either the spouse or child of the stroke survivor. Stroke patients had a median age of 70 years, range 53-85 years. All participants reported having experienced emotional stress and depressive symptoms as a result of caregiving. Although most had family support, reduced social activities and added unanticipated financial burdens increased caregiver's stress. None of the caregivers had received training in post-stroke care tasks after the patient's discharge and only a few had received some psychological support, yet almost all expressed the need to see a professional to improve their mental health. Keeping a positive attitude towards their relative's physical post-stroke condition was a key coping mechanism. CONCLUSIONS: In the absence of structured institutional responses, family members endure with the provision of care for stroke survivors, a task escorted by major emotional, financial, and social strains. This burden could be prevented or curtailed if caregivers were to be targeted by interventions providing psychological and financial support, together with basic training on post-stroke care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".