Endurance, resistance and resilience in the South African health care system: case studies to demonstrate mechanisms of coping within a constrained system
Bibliographic record
Abstract
BACKGROUND: South Africa is at present undertaking a series of reforms to transform public health services to make them more effective and responsive to patient and provider needs. A key focus of these reforms is primary care and its overburdened, somewhat dysfunctional and hierarchical nature. This comparative case study examines how patients and providers respond in this system and cope with its systemic demands through mechanisms of endurance, resistance and resilience, using coping and agency literatures as the theoretical lenses. METHODS: As part of a larger research project carried out between 2009 and 2010, this study conducted semi-structured interviews and observations at health facilities in three South African provinces. This study explored patient experiences of access to health care, in particular, ways of coping and how health care providers cope with the health care system's realities. From this interpretive base, four cases (two patients, two providers) were selected as they best informed on endurance, resistance and resilience. Some commentary from other respondents is added to underline the more ubiquitous nature of these coping mechanisms. RESULTS: The cases of four individuals highlight the complexity of different forms of endurance and passivity, emotion- and problem-based coping with health care interactions in an overburdened, under-resourced and, in some instances, poorly managed system. Patients' narratives show the micro-practices they use to cope with their treatment, by not recognizing victimhood and sometimes practising unhealthy behaviours. Providers indicate how they cope in their work situations by using peer support and becoming knowledgeable in providing good service. CONCLUSIONS: Resistance and resilience narratives show the adaptive power of individuals in dealing with difficult illness, circumstances or treatment settings. They permit individuals to do more than endure (itself a coping mechanism) their circumstances, though resistance and resilience may be limited. These are individual responses to systemic forces. To transform health care, mutually supportive interactions are required among and between both patients and providers but their nature, as micro-practices, may show a way forward for system change.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 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".