Middelenmisbruik onder de Belgische beroepsbevolking. Of spreken we beter over werkgerelateerd alcohol- en druggebruik?
Bibliographic record
Abstract
The impact of substance abuse in society is considerable, but depends largely on the type of drug used. Alcohol consumption was the third leading risk factor in the Global Burden of Disease Study 2010 of the World Health Organization (WHO). It plays a role in more than 60 major diseases and injuries. Alcohol-related health damage can result from occasional or regular heavy drinking. Cannabis is by far the most frequently used illegal drug in Europe. Estimations of the lifetime use of cocaine, amphetamines, and ecstasy are considerable lower. In Europe the high average consumption of benzodiazepines is a largely unrecognized problem. The workplace is confronted with the negative consequences of substance abuse. In the European Union the tangible costs of alcohol in 2010 were estimated to be €74.1billion, which is 47% of the total social cost. This is the result of lost productivity through absenteeism, unemployment and lost working years because of premature death. Alcohol-related work performance problems are mainly associated with non-dependent, lower-level drinkers who represent the biggest group of drinkers. Recreational drug use may also reduce performance efficiency and safety at work, but more research is needed in this area. The impact of benzodiazepines has mostly been described in relation to its impact on driving. Following a Collective Labour Agreement (CLA number 100), all private organizations in Belgium must have a policy statement on alcohol and drugs in the workplace. This CLA also promotes the development of an appropriate prevention policy. However there is a lack of prevalence data concerning the use and problematic use of alcohol and other drugs among the working population in Belgium. There is also little known about the motivation and approach of Occupational Physicians in the prevention and management of substance abuse among employees. References Lim, S.S. et al. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380: 2224-60. Gisle, L., Hesse, E., Drieskens, S., Demarest, S., Van der Heyden, J., & Tafforeau, J. (2010). Health Interview survey, 2008. Brussels: Scientific Institute of Public Health. World Health Organization (2014). Global Status Report on Alcohol and Health. Geneva: WHO. UNODC (2013). World Drug Report 2013 (United Nations publication, Sales No. E.13.XI.6). INCB (2010). Report of the International Narcotics Control Board for 2009 (United Nations publication, Sales No. E.10.XI.1). Rehm, J., Shield, K.D., Rehm, M.X., Gmel, G. & Frick, U. (2012). Alcohol consumption, alcohol dependence and attributable burden of disease in Europe: Potential gains from effective interventions for alcohol dependence. Toronto: Centre for Addiction and Mental Health. Smith, A. et al. (2004). The scale and impact of illegal drug use by workers. Centre for Occupational and Health Psychology. Cardiff: Cardiff University. Orriols, L. et al. (2009). The impact of medicinal drugs on traffic safety: a systematic review of epidemiological studies. Pharmacoepidemiology and Drug Safety, 18 (8), 647-658. WHO Regional Office for Europe (2013). Status Report on Alcohol and Health in 35 European Countries 2013. Copenhagen: WHO [http://www.euro.who.int/__data/assets/pdf_file/0017/190430/Status-Report-on-Alcohol-and-Health-in-35-European-Countries.pdf]. Download 15/12/2013. http://www.qado.be/media/37364/q-ado_resultaten_3jaar%20na%20cao%20100_2april13.pdf http://www.nar.be/CAO-COORD/cao-100.pdf. Use of alcohol, illegal drugs, hypnotics and tranquilizers in the Belgian population (UP TO DATE) [http://www.belspo.be/belspo/fedra/proj.asp?l=en&COD=DR/60] Royen, K., Remmen, R., Vanmeerbeek, M., Godderis, L., Mairiaux, P. & Peremans, L. (2013). A review of guidelines for collaboration in substance misuse management. Occupational Medicine, 63: 445-447. The I-Change Model [http://www.personeel.unimaas.nl/hein.devries/I-Change.htm] Download 10/12/2013. Smith, A.J. (2011). Evaluating the contribution of interpretative phenomenological analysis. Health Psychology Review, 5, (1) 9-27.20. Larkin, M., Watts, S. & Clifton, E. (2006). Giving voice and making sense in interpretative phenomenological analysis. Qualitative Research in Psychology, 3, 102-120.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".