Oppression and internalized oppression as an emerging theme in accessing healthcare: findings from a qualitative study assessing first-language related barriers among the Kurds in Turkey
Bibliographic record
Abstract
BACKGROUND: Language has been well documented to be a key determinant of accessing healthcare. Most of the literature about language barrier in accessing healthcare is in the context of miscommunication. However, it is critical to consider the historical and political contexts and power dynamics underlying actions. The literature in this matter is short. In this paper we aimed to find out how first-language affects access to healthcare for people who do not speak the official language, with a particular focus on language oppression. METHODS: We conducted this qualitative study based on patient-reported experiences of the Kurds in Turkey, which is a century-long oppressed population. We conducted 12 in-depth interviews (all ethnically Kurdish, non-Turkish speaking) in Şırnak, Turkey, in 2018-2019 using maximum variation strategy. We used Levesque's 'Patient-Centred Access to Healthcare' framework which addresses individual and structural dimensions to access. RESULTS: We found that Kurds who do not speak the official language face multiple first-language related barriers in accessing healthcare. Poor access to health information, poor patient-provider relationship, delay in seeking health care, dependence on others in accessing healthcare, low adherence to treatments, dissatisfaction with services, and inability to follow health rights were main issues. As an unusual outcome, we discovered that the barrier processes in accessing healthcare are particularly complicated in the context of oppression and its internalization. Internalized oppression, as we found in our study, impairs access to healthcare with creating a sense of reluctance to seek healthcare, and impairs their individual and collective agency to struggle for change. CONCLUSIONS: A human-rights-based top-down policy shift, and a bottom-up community empowerment approach is needed. At the system level, official recognition of oppressed populations, acknowledgement of the determinants of their health; and incorporating their language in official capacities (particularly education and healthcare) is crucial. Interventions should include raising awareness among relevant professions and stakeholders that internalized oppression is an issue in accessing healthcare to be considered. Given that internalized oppression can be in other forms than language or ethnicity, future research aimed at examining other aspects of access to healthcare should pay a special attention to internalized oppression.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".