Identifying Barriers in Access to Care for Head and Neck Cancer Patients: A Field Study in Dakar
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To identify barriers in access to care for head and neck cancer (H&NC) patients in low- and middle-income countries (LMICs), specifically within Dakar, Senegal, using both quantitative and qualitative data. STUDY DESIGN: Descriptive observational study. METHODS: Patients with H&NC were selected from two independent university hospitals in Dakar, Senegal. A mixed-methods descriptive study was performed using a specifically tailored questionnaire and a focused ethnographic qualitative approach to identify factors that delay patient presentation, referral, and treatment. Quantitative data were analyzed using descriptive statistics and qualitative using a deductive approach based on a systematic review of the literature. RESULTS: Thirty-three patients with a mean age of 57.8 years were included. Presentation delay was 5.7 months, mainly attributed to cost of consultation (39%), waiting time at doctor's office (15%), and distance to healthcare facility (12%). Referral delay greater than 3 months was observed in 60% of participants, secondary to misdiagnosis and lack of appropriate referral. Treatment delay was associated with limited local treatment capacity and securing cost of treatment. Cost of transportation impacted all delays. CONCLUSIONS: This work used an evidence-based approach to identify barriers in access to care for H&NC patients in sub-Saharan Africa. It suggests the feasibility and transferability of this methodology which combined a quantitative approach based on the literature with a qualitative analysis. Insight provided by this study will be used to guide development of implementation strategies for early detection of H&NC in LMICs. LEVEL OF EVIDENCE: 4 Laryngoscope, 132:1219-1223, 2022.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".