Bibliographic record
Abstract
Traumatic brain injury (TBI) is an important public health problem. Throughout the world, TBI is one of the leading causes of life-long disability. The impact on the injured person, his or her family, friends, as well as society, and the healthcare system is well known, and so is the need for prevention, efficient acute care, rehabilitation, and follow-up. This special issue brings together articles from 5 countries—Canada, Denmark, Sweden, the Netherlands, and the United Kingdom—and provides a comprehensive insight into TBI rehabilitation within different healthcare systems around the world. Each article focuses on specific aspects of TBI rehabilitation, strengths and weaknesses, and future areas of interest as well as problems to address. In the first article, the Canadian system is described. Canada is the second largest country in the world with a population of only 31 million. The country has many similarities but it also differs substantially from its North American counterparts as it has a healthcare system based on principles of universal access to healthcare, making basic healthcare services available to all residents of the country. Even though TBI rehabilitation has developed greatly over the last 20 years in Canada, several areas of improvement are listed. Interestingly, many of them are similar to those listed in the other countries in this issue, emphasizing the similarities rather than the differences between TBI rehabilitation across the world. In the second article, the experiences from Denmark and the establishment of a national strategy for treatment and rehabilitation of TBI are presented. The vision was to create a system for tax-financed rehabilitation, centralized to 2 units, each with half the country as uptake area. The advantages gained by this centralization are described together with the results for the first 3 years, which indicate that outcome is improved. In the third article, TBI rehabilitation in Sweden is described. Demographics and injury characteristics of TBI in Sweden follow those of Europe in general. Several new developments, among them the decision to recommend that all rehabilitation departments in Sweden go through the accreditation process of the Commission of Accreditation of Rehabilitation Facilities, are expected to lead to improved services. In the fourth article, the system in the Netherlands is presented. Awareness of the impact of TBI has grown rapidly in the last 15 years in the Netherlands, and initiatives to improve services have been taken. Cognitive rehabilitation is such an example, and since 1993, healthcare insurers have agreed to pay for cognitive rehabilitation programs. However, like those of many other countries, several issues that need to be resolved are also described. In the fifth and final article, TBI rehabilitation in United Kingdom is presented. Rehabilitation has grown over the last 20 years. Various actions, some fairly recent, have been taken by the UK Government, which, as is described, means that it is essentially the first time that rehabilitation has featured in any major policy development. At the same time, new funding arrangements pose a considerable threat for rehabilitation services as currently set up in the United Kingdom. This special issue intends to present and compare rehabilitation of persons with TBI in different parts of the world. It is our hope that the articles will broaden our knowledge and that they may serve as an inspiration for clinicians and researchers in their ambition to develop efficient TBI rehabilitation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.179 | 0.041 |
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".