Assessment of the Frequency and Pattern of Outbound Medical Tourism in Government-Owned Hospitals in the Federal Capital Territory (FCT), Nigeria
Bibliographic record
Abstract
Outbound medical tourism is becoming common in Nigeria as patients are frequently sent to other countries for specialized care.The frequency and pattern of Outbound medical tourism help to direct the investments in medical tourism.This study sought to assess the frequency and pattern of outbound medical tourism in government-owned Hospitals in the Federal Capital Territory (FCT), Nigeria.This was a descriptive cross-sectional study conducted among 160 medical doctors who had made referrals for medical tourism in other countries.A multi-stage sampling technique was used, and data was collected using an Interviewer-administered structured questionnaire.In the last 12 months, 227(36.15%)respondents had not done any referral, In the last 6 months, 381 (60.67%) had not done any referral outside the country, 215 (34.24%) had referred one (1) patient in the last 6 months 32 (5.1%) had done two (2) referrals.Commonest destinations included India 268 (42.68%),USA 98 (15.61%),UAE 71 (11.31%),UK 68 (10.83%),Saudi Arabia 42 (6.69%),Germany 27 (4.3%),Israel 18 (2.87%),Egypt 12 (1.91%),Canada 10 (1.59%), Singapore 8 (1.27%).Sixty-six percent of the clients selected their destination themselves, while 211 (33.60%) were selected by the doctors.The Top five referrals done by doctors in the last 12 months include Childbirth 53 (13.22%),Transplantation surgery 112 (27.93%),Ophthalmological surgery 44 (10.97%),Checkup 42 (10.47%),and Diagnostics 17 (4.24%).Improvements in local healthcare services will reduce outbound bound medical tourism and put Nigeria in a position to gain from inbound medical tourism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".