Bibliographic record
Abstract
There are sounds that are as familiar to family physicians as their own voice: the wail of a youngster as a needle punctures skin, the happy cry of a woman when she learns she's pregnant, the quiver of concern in the voice of a patient with unexplained pain. For Dr. Lori Burgess, family practice has also brought the sound of machine-gun fire, exploding land mines, jeeps bouncing over rocky paths, rebels stealing through the night. Last year, the Kentville, NS, FP left the comfort of practice in small-town Canada to spend 6 months in Sri Lanka, a country ravaged by civil war for nearly 20 years. A volunteer with Doctors Without Borders/Medecins Sans Frontieres (www.msf.ca), Burgess was sent to the northern part of Sri Lanka, an area controlled by the Tamil Tigers. These rebels who have been fighting for a separate homeland for the country's Tamil minority since 1983. Day in, day out, the machinery of war operated outside the 32-bed hospital where Burgess worked 13-day shifts. But both the army and the rebels left the hospital and its staff alone. “They recognized the value of what we were doing.” Much of what Burgess did each day dealt with the ravages of war. The scars were deep and the damage sustained. Today there is no electricity, no phones, no paved roads, few jobs. “A lot of men are unemployed,” notes Burgess. “We saw a fair bit of post-traumatic stress disorder. [These people] have lost members of their families, had beatings, seen suicides.” Ultimately, though, war was not life. Even with armed rebels and national troops a constant presence, Burgess says there was still of sense of joy. “In general, the people are resilient and happy. People still went about their business.” Burgess also went about hers, but the business of medicine is much different in Sri Lanka than Nova Scotia. There was only one other physician to serve 20 000 people, and “there was no lab, no x-ray facilities, few medicines. You couldn't rely on tests. You had your stethoscope, your othoscope, an old EKG machine, your brain.” When her brain told her that further exploration was critical, Burgess would send a patient south to see specialists. However, this time-consuming trip could be a waste of time. “Often,” she says, “the patient was sent back with nothing done.” This inability to help – the natural consequences of war and poverty – were what Burgess found most frustrating. “It is discouraging to know you could have done something if conditions had been different.” Distance and accessibility were major problems. “People had to travel so far to get to us. They didn't always make it.” On average, the doctors and nurses at the hospital treated 200 inpatients a month and 1200 outpatients. Patients arrived by bike, in carts and on foot. But conditions may soon change in this beautiful country. Guns have fallen silent in the wake of a ceasefire deal signed Feb. 22, which moves the country one step closer to peace talks. Sri Lanka is the third place Burgess has volunteered. In 1994 she worked in Kenya, and 3 years later she went to Guatemala. More missions are on the horizon, although where and when remains uncertain. But the trips don't get any easier. “After coming home,” she says, “it takes months to reacclimatize.” — Donalee Moulton, Halifax
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".