Notice bibliographique
Résumé
The usual chatter around the nursing station had been replaced by the eerie sounds of the backup generators turning on and off as the wildfire burned through power lines on its way toward our rural 10-bed hospital in northern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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,010 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».