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Record W3153510722 · doi:10.1186/s12913-021-06376-6

Exploring patient-provider interactions and the health system’s responsiveness to street-connected children and youth in Kenya: a qualitative study

2021· article· en· W3153510722 on OpenAlexafffund
Lonnie Embleton, Gayapersad Allison, Reuben Kiptui, David Ayuku, Apondi Edith, Paula Braitstein

Bibliographic record

VenueBMC Health Services Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCanada Research ChairsPublic Health OntarioUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institutes of HealthUniversity of Toronto
KeywordsThematic analysisQualitative researchContext (archaeology)Health informaticsHealth administrationNursing researchPublic healthMedicineHealth careHealth services researchNursingFocus groupPublic relationsSociologyEconomic growthPolitical scienceSocial science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0110.008
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.250
GPT teacher head0.532
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes2
Has abstractyes

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