Exploring patient-provider interactions and the health system’s responsiveness to street-connected children and youth in Kenya: a qualitative study
Bibliographic record
Abstract
BACKGROUND: In Kenya, street-connected children and youth (SCY) have poor health outcomes and die prematurely due to preventable causes. This suggests they are not accessing or receiving adequately responsive healthcare to prevent morbidity and mortality. We sought to gain insight into the health systems responsiveness to SCY in Kenya through an in-depth exploration of SCY's and healthcare provider's reflections on their interactions with each other. METHODS: This qualitative study was conducted across 5 counties in western Kenya between May 2017 and September 2018 using multiple methods to explore and describe the public perceptions of, and proposed and existing responses to, the phenomenon of SCY in Kenya. The present analysis focuses on a subset of data from focus group discussions and in-depth interviews concerning the delivery of healthcare to SCY, interactions between SCY and providers, and SCY's experiences in the health system. We conducted a thematic analysis situated in a conceptual framework for health systems responsiveness. RESULTS: Through three themes, context, negative patient-provider interactions, and positive patient-provider interactions, we identified factors that shape health systems responsiveness to SCY in Kenya. Economic factors influenced and limited SCY's interactions with the health system and shaped their experiences of dignity, quality of basic amenities, choice of provider, and prompt attention. The stigmatization and discrimination of SCY, a sociological process shaped by the social-cultural context in Kenya, resulted in experiences of indignity and a lack of prompt attention when interacting with the health system. Patient-provider interactions were highly influenced by healthcare providers' adverse personal emotions and attitudes towards SCY, resulting in negative interactions and a lack of health systems responsiveness. CONCLUSIONS: This study suggests that the health system in Kenya is inadequately responsive to SCY. Increasing public health expenditures and expanding universal health coverage may begin to address economic factors, such as the inability to pay for care, which influence SCY's experiences of choice of provider, prompt attention, and dignity. The deeply embedded adverse emotional responses expressed by providers about SCY, associated with the socially constructed stigmatization of this population, need to be addressed to improve patient-provider interactions.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".