Notice bibliographique
Résumé
In the February issue of the Canadian Journal of Surgery (CJS), Dr. Waddell challenged CJS readers to consider the mismatch between physician numbers and patient demand and to reflect on ways to make access to surgery sustainable.1 A useful approach to this problem is to examine the association between the need for health care and the use of services provided by doctors. Nabalamba and Millar2 recently reviewed public access to doctors in Canada, as determined by the 2005 Canadian Community Health Survey.3 In this survey, the authors reported on Canadians' access to generalist and specialist physicians, based on the following 3 factors of need: state of health and illness acuity, predisposition to using services (e.g., age, sex, ethnicity) and enabling factors (e.g., education, income, and access to health providers and health facilities).4 Data for access to specialists merits attention, since they project the demand for services from our surgical colleagues. The data showed that 77% of Canadians aged 18 to 64 years and 88% of seniors reported consulting with a general practitioner (GP) in the previous year; corresponding numbers for specialists were 27% and 34%. It is reassuring that individual health need was a strong determinant for the use of services provided by doctors. When need was taken into account, physician consultations were independently associated with age, sex, household income, ethnicity, language, place of residence (rural v. urban) and having a regular GP. People over the age of 75, rural residents, visible minorities and Aboriginal people had low odds of obtaining specialist consultations. What does this portend for surgical specialists in the future? In concordance with health need as a strong determinant of access to surgeons, the article by Gaudet and others5 in this issue of the CJS shows that patients treated earlier with total hip replacement surgery had higher symptom scores.5 This demonstrates that prioritization of care for services with priority scoring tools and wait-time targets will play a role in resource allocation for surgical care in the future. Surgeons need to familiarize themselves with these tools to support quality and timely access. Interestingly, Gaudet and colleagues5 showed no association of age, sex and occupation with wait-time for arthroplasty care. The report by Nabalamba and Millar2 shows different data. Elderly people were shown to have access to family physicians, but their access to specialists was proportionately low. The aging baby boom population is not likely to tolerate this pattern. Specialists can expect this population to have high demands for such problems as fragility fractures and osteoarthritis. These have recently been addressed through wait-list funding to augment surgical services; however, targeted interventions impact on other surgical services that do not receive augment funds. This translates to surgeons operating from multiple sites (including private care facilities) instead of traditional hospital sites. These practice changes must be met by surgical teamwork and careful patient care handovers. In academic centres, the impact on surgical trainees must be taken up by innovative teaching, such as simulation. A chief enabling factor that facilitates access to specialists is ready access to a regular GP. However, it is estimated that 3.5 million Canadians do not have a regular GP. This issue has been addressed by increasing medical undergraduate and post-graduate enrolments and by the development of new models of medial education, for example, the distributed medical education programs at the Northern Ontario School of Medicine and at the University of British Columbia. Strategically aimed at improving access to family physicians in remote communities, distributed medial education programs will bring new pressures for surgical specialists. Developing education programs may conflict with the need for high service volumes, unless funding is also provided for quality education for new trainees and time is permitted to enable surgeons to teach their trainees in the operating room. Surgeons may be altruistic when offering to teach, but pressures to deliver service add new responsibilities. Can alternative providers such as nurse practitioners, advanced care physiotherapists and physician assistants help to ensure the best navigation throughout the system? These training programs also are meeting challenges in keeping up with demand. This will require sophisticated professional communication with surgeons, and issues over liability must be tackled. The dialogue will continue into the future to ensure that the Canada Health Act will maintain access to publicly funded, medically necessary health care that is free of financial or other barriers. Surgeons must accommodate novel models to deliver their services based on burgeoning health need as well as predisposing and enabling factors that determine Canadians' wishes for optimal health care. Hopefully, this can occur without “burnout”! Garth L. Warnock, MD Coeditor
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,299 | 0,133 |
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 ».