Bibliographic record
Abstract
The usual chatter around the nursing station had been replaced by the eerie sounds of the backup generators turning on and off as the wildfire burned through power lines on its way toward our rural 10-bed hospital in northern Alberta, Canada. The sky seemed to change hourly between dark gray and orange, accompanied by showers of ashes and the angry roar of the approaching fire. The waiting room television blared periodically with the emergency alert evacuation order that had been issued the previous day. Most hospital staff had evacuated along with the general public, leaving behind a volunteer skeleton crew that had spent the night at the hospital. When I came on call that morning, 1 of the 2 highways had become inaccessible, leaving 1 remaining exit from town. The skeleton crew still at the hospital took extra precautions because if the wildfire took down the cell towers, there would have been no way to notify each other of the need to evacuate immediately. That night, my nonphysician husband and I slept in an empty patient room, surrounded by our most prized earthly possessions, including my wedding dress, our photo albums, and some clothes which we had hastily packed the day before. The McMillan Complex wildfire of May 2019 would eventually burn through 273,000 hectares, becoming one of the largest wildfires in Alberta’s history. Early in my training, I had been attracted to the image of the small-town doctor who, in my mind, embodied generalism—the care of all patients, for all conditions, at all times. Two years out of training, I had taken up a practice in a northern Canadian Indigenous community, became the community medical director, and found myself on call during a state of emergency. My responsibilities had somehow ballooned from full-scope family medicine (inpatient care, outpatient care, emergency care, occasional obstetrics, and home visits) to include hospital administration, interprofessional education, physician recruitment and retention, and now, disaster response. The scope of rural medicine is already daunting, but the added responsibilities of sustaining health care in a marginalized and underserved community were overwhelming. I thought that surely my foray into rural generalism had toed the line between courage and foolishness and had spat me out on the wrong side. I was attracted to rural generalism out of a desire to be the synthesizer; the health care resource optimizer; the teacher and advocate; and a medical resource for a community in need. Yet, I often found myself working on the outer edges of my comfort zone, unsure of how close I was to the edge of a cliff. I wanted to think that my rapid immersion into the frightening unknown of rural generalism was the antidote to an increasingly siloed health care system. The nursing station telephone rang. It was a representative from the Emergency Operation Center who informed us, “Your hospital is now on standby.” The charge nurse hung up the receiver and relayed the message to the skeleton crewmembers. We exchanged nervous looks with one another, unsure of what it meant to be “on standby.” “Well, at least the hospital is made of brick,” one of the nurses stated in a matter-of-fact tone. We erupted into laughter as the gallows humor had temporarily alleviated our anxieties. Later that day, an order was issued for the skeleton crew to evacuate and shut down all services. Thankfully, all inpatients had been flown out the day before to surrounding hospitals. The remaining tasks were to shut off all power, gas, and oxygen lines and to move the oxygen tanks to a sectioned-off part of the hospital, minimizing structural damage in the case that the tanks exploded. The evacuation lasted several weeks, including a staged reentry. There were no fatalities and no structures were lost, although the temporary displacement affected the elderly and indigent populations for months afterward. In the following months, I started noticing a subtle change in the way my patients and the community saw me. I went from being called the doctor to our doctor. Our garbage man personally brought chopped wood over to our house when he heard that our furnace had malfunctioned. Doctors in the next town heard about our struggles with obstetrical care and offered to develop a referral pathway. Other hospitals took notice of our grassroots interprofessional simulation program and began developing their own. Little did we know at the time that many of these relationships would be instrumental in pandemic planning just a few short months after the wildfire. When it came time to move away from that town, a colleague gifted me with a traditional ribbon skirt and told me, “Remember to dance with our people, wherever you are.” Her gift brought tears to my eyes along with a moment of clarity; my fears and self-doubt were replaced by an overwhelming appreciation for the relationships that were forged during my brief foray into rural generalism. Acknowledgments: The author thanks Monika Johnson, RN, Kristin Simard, RN, and Sheena CarlLee, MD, for their comments. The author also acknowledges the peoples and land of the Bigstone Cree Nation mentioned in this story.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".