Bibliographic record
Abstract
Government of Canada 加拿大宣布创立精神健康研究网络 6月13日，加拿大卫生研究院（Canadian Institutes of Health Research，CIHR）院长Alain Beaudet博士和Graham Boeckh Foundation（GBF）基金会主席J. Anthony Boeckh共同宣布启动ACCESS Canada（Adolescent/young adult Connections to Community-driven Early Strengths-based and stigma-free Services）。 ACCESS Canada是由青少年精神卫生转化研究项目（Transformational Research in Adolescent Mental Health，TRAM，由CIHR和GBF合作创立）主导建立的研究网络。其目标是5年内为患有精神疾患的青少年提供有研究基础的干预手段，改善该群体预后，并最终使加拿大整体精神卫生现状得到改观。 在加拿大，约有五分之一的人在一生中会经历至少一次精神疾病发作。在全部年龄段中，青少年所占比例最大，约有75%的精神心理障碍或疾病在25岁前首次发病，同时超过一半患者首次发作年龄在11到25岁之间。虽然青少年比其他任何群体都更有可能罹患精神疾病，但不幸的是，他们接受精神卫生服务的机会最少。因为现有的干预绝大部分是为更幼小的儿童和更年长的成年人提供的，这意味着加拿大精神卫生系统在本应该最强大的地方最脆弱。其结果是精神疾病正在给青少年及其家庭造成重创。 ACCESS Canada网络将患者与研究人员、健康护理专家及政策制定者联系起来，力图弥补基础研究与干预实践和相关政策之间的空白。把有价值的干预手段带到临床一线，使患者及其家庭从研究中获益。它代表了一种整合了多省、多领域及不同合作者的多级合作模式，从扩大资源和资助研究等角度为最终改善加拿大精神健康干预体系出资出力。 ACCESS Canada的目标是： 1. 改善青少年对精神健康问题的关注与认识，实现疾病的早期诊断； 2. 尽快让青少年接受适宜的、尊重事实和证据的且友好的精神卫生干预。 另外，通过CIHR，加拿大政府将在5年内资助TRAM 1250万美元。Graham Boeckh Foundation同时匹配同等金额，总投资将达到2500万美元。 原文标题：CIHR and Graham Boeckh Foundation launch mental health research network 原文链接：http://news.gc.ca/web/article-en.do?nid=856679 原文标题：Fact Sheet - ACCESS Canada, a research network developed by TRAM - Transformational Research in Adolescent Mental Health 原文链接：http://news.gc.ca/web/article-en.do?nid=856669 检索日期：2014.6.20
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".