Notice bibliographique
Résumé
There is a growing consensus that most Organisation for Economic Co-operation and Development (OECD) nations have more than enough doctors to meet health needs, but that some need is not met because of the distribution of these doctors, from a training to service perspective, and demographically, ethnically, geographically and disciplinarily.1 Most governments, health administrators and media do not appear to understand the situation well as the usual response is to advocate for and to increase the overall doctor supply.2, 3 This is expensive, distracts funding from processes to understand the health need, and the consequent development of innovative models of care, such as virtual healthcare approaches, may compromise the quality of doctor experiential (i.e. apprenticeship) training and actually reduce health system efficacy, and, with three probable exceptions,4-6 has not been successful. Canada and Australia are current examples of a consequent over-supply of doctors viewed from a system learner- and employment-capacity viewpoint.2, 7 The ‘mal-distribution’ consensus is largely presumptive, at least in part because the need for doctors is not known explicitly for any health system and given that the need for doctors is entirely dependent on what the doctors actually do.8 There is certainly no basis for arguing that there is a global shortage of doctors in the OECD nations. For example, unmet health need is measured explicitly in the European Union (EU).9, 10 In countries such as France, Germany, the Netherlands, Sweden, Switzerland and the UK, most unmet health need is due to non-health system factors. In addition, comparing doctor to population ratios and capitated health funding for these countries, strongly suggests that significantly increasing the total number of doctors or the global health budget, in isolation, would not reduce the unmet health need arising due to service affordability, accessibility, availability and acceptability factors. Doctor to population ratios, considered regionally, ethnically or socio-economically, provide only weak support for the case that there is a ‘mal-distribution’ of doctors. The weakness arises, as the desirable ratio is unknown such that perceived shortages may be artificial constructs given that the comparative norm might represent an actual over-supply. More impressive support for a ‘mal-distribution’ is derived from considering disparities in: disease detection and intervention rates; outcomes such as survival and morbidity; unemployment rates due to health issues; and, even in gross measures, of which life expectancy is the most obvious.11-13 Three conclusions are possible. First, an explicit understanding of health need and the generators of unmet need are essential for any sensible health investment.14 Second, the role of the doctor needs to be a positive construct and not a deconstruction of current roles8, which too often results in doctors surrendering functions that they find undesirable and/or unprofitable. A noteworthy, but yet to be published, experience in a region of apartheid-era South Africa, was that effective public health was provided for as many as 4 million people by 40 doctors or less. The role of these doctors was constructed by way of identifying only those tasks that someone who was a doctor could undertake – eventually largely confined to patient differentiation and making clinical decisions under conditions of uncertainty. The subject of this editorial is the third conclusion. There are at least seven steps, which will ‘redistribute’ doctors to meet health needs better and it is probable that all seven will require attention for any likely success. What is also apparent is that although an over-supplied medical labour market creates a desirable milieu for changing how medical services are funded and will reduce reliance on international medical graduates, it will not, by itself, generate a desirable redistribution of doctors.2, 7, 15 The seven steps will be discussed here in chronological career order, rather than in any order of likely impact. Step 1: Recruit medical students who are most likely to take up ‘desirable’ careers and to work in locations where there is a high level of unmet health need.16, 17 The best data in this context exist for preferential admission schema for regional and rural-origin students and subsequent uptake of careers with a general scope of practice in regional and rural settings.18, 19 However, the three most impressive examples of this – the Northern Ontario School of Medicine (NOSM), James Cook University in Queensland and Flinders University of South Australia – have combined appropriate selection processes with a significant investment in regional and rural clinical training infrastructure, are very well led and exist in locations where there is limited access to urban-based commonly sought-after careers in procedural medicine and surgery.4-6 The point is that their success would have been extremely unlikely on the basis of their student selection processes alone. There is strong ideological support for preferential admission of indigenous students and for ethnic minorities for whom there is a significant unmet health need.11 Currently, there are no data to show these schema have improved the health and well-being of the targeted communities. Medical student selection processes must have high face validity to help manage the tension between societal expectations of a meritocracy and societal acceptance of preferential admission to achieve health equity and consequently, need to be correlated with health outcome measures.16 Step 2: Employ a pedagogical approach to medical student education that showcases ‘desirable’ careers and locations where there is a high level of unmet health need.19-21 Again, the data are weak in this context, but there is at least an anecdotal, logical and generally acceptable argument that positive role models and educational ‘immersion’ programmes are influential – bearing in mind that many medical students make career choices after they graduate.22 Step 3: Ensure that all medical graduates are exposed to ‘desirable’ careers and locations where there is a high level of unmet health need very early in their postgraduate careers (i.e. internship). The requirement here is also for real immersion experiences and positive role models and attends to the observation made above about when career choices are often made.22 Step 4: Employ postgraduate scholarship approaches that are based on sound behavioural economics principles.23 An illustration of this step is the difference in impact that was experienced in New Zealand between a voluntary bonding scheme (i.e. student debt forgiveness for the uptake of an advocated vocational training position or a work location considered to be vulnerable in regard to healthcare) and advanced training fellowships (i.e. funding made available for targeted training, often internationally, in the context of an ongoing employer commitment). The debt-forgiveness bonding scheme was not popular amongst medical graduates and feedback was predictable in that the scheme resulted in stigmatisation of the selected vulnerable specialties and work locations. Most students taking up the scheme were already committed to such a career so that the money was spent on encouraging them to do something that they were going to do anyway. By contrast, the demand for the fellowships was very high and considered to be an advocacy of both the trainee and the eventual role. That is, the latter had a predictably positive impact on personal and career status. Step 5: Ensure that postgraduate vocational training schemes are available in locations where there is a need for such doctors. The success of the NOSM in regard to long-term career outcomes is highly dependent upon vocational training being available in Northern Ontario in medicine, surgery and psychiatry, as well as in family medicine – with only short-term attachments in urban centres for some experience in high-technology medicine and for exposure to rare conditions.4 The importance of this is not only does it train doctors in roles and where they are needed, but also this a stage of many doctors’ lives when they are making long-term lifestyle decisions, such as entering into relationships, having children, buying property and so on. That is, this step constitutes a form of social engineering. It is encouraging to see that this approach to vocational training is also being taken up by James Cook University, at least in the first instance in general medical practice. Step 6: Ensure that careers, which are being advocated for, are as attractive as is possible.13, 24, 25 The core factors here include possible and usual career progression, aligned training and professional development, the scope of possible and usual practice and the underpinning business models; all of which contribute to career status. Status matters – long-term advocacy for a career type requires an address of how the career is viewed by other doctors and by the community. The professional and societal biases are usually aligned. Step 7: Ensure that the locations where doctors are needed are as attractive as possible to live.13, 24, 25 There is a need for collaborative work with regional authorities to address issues such as the availability of jobs for spouses and partners, access to acceptable schooling for children and even an attention to the local housing and property market. It is a reasonable assumption that doctors in most OECD nations could be better distributed to meet health need. To achieve a more effective alignment of where and how doctors practise will require a comprehensive approach, along the lines of the seven steps cited here. Certainly, piecemeal approaches have not proven successful in the past and there is no reason to expect isolated endeavours will be useful in the future.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».