A Registry Based Approach to Suicide Research: Opportunities and Limitations in the Norwegian Population Representative Registries
Bibliographic record
Abstract
Together with the other Nordic countries, Norway stands in a unique position internationally with its large population representative registries. By means of unique personal identification numbers assigned to all Norwegian citizens, as well as to immigrants who stay for more than 6 months, it is possible to construct individual record linkages covering an increasing number of years across different national registries. The Norwegian registries include, among others, information from the primary and specialist health care services, the prescription of drugs, and causes of death. In addition, they include sociodemographic information like year of birth, gender, immigration status, educational attainment, marital status, and the use of various social benefits. Norway is one of very few countries that have a nationwide registry on primary health care use. This registry gives the opportunity to explore the role of the primary health care services prior to suicide and in the follow-up of the suicide bereaved, which has been pointed out as one of the most promising areas for future suicide prevention. Linkages of Norwegian registries opens up new approaches in analyses and the possibility to explore a range of novel research themes, such as treatment trajectories and patterns of health care use prior to suicide and among the suicide bereaved. In this paper, we give a description of the Norwegian population representative registries applicable for suicide research. We discuss the analytic opportunities as well as the challenges and obstacles of a registry based research approach to suicide. The main strength of registry-based research on suicide is the ability to maintain data on the total population, the possibility to study small sub-populations or low-prevalent events, virtually continuous timelines in longitudinal data, few or no non-response or other missing data, no sample attrition, and the possibility of gaining access to large amounts of various health and sociodemographic information. In addition registry-based research allows investigation of hard-to-reach populations, such as groups of individuals with severe mental disorders or immigrants that traditionally have been difficult to recruit for participation in research projects. The opportunities presented in the article could motivate to do similar research in Canada and even inspire for cooperation between Norwegian and Canadian researchers on registry based research on suicide. In our opinion, registry-based research on suicide will play an increasingly important role in suicide research in the years to come.
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 | 0.384 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".