Effect of a cancer diagnosis on the divorce rate
Bibliographic record
Abstract
Objective: Cancer is a burden for patients and their partners. Especially the long-term effects of a cancer diagnosis on relationships are not clear. Literature is inconsistent regarding the assumption of a higher divorce rate following a cancer diagnosis. A systematic review dedicated to this question has not yet been completed and will be addressed with this work. Methods: The systematic review will be conducted according to the guidelines of the Cochrane Collaboration and the PRISMA statement. The following electronic databases will be searched: Web of Science (including the following databases: Web of Science Core Collection, BIOSIS Citation Index, BIOSIS Previews, Current Contents Connect, Data Citation Index, Derwent Innovations Index, KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index, SciELO Citation Index, Zoological Record), Ovid SP MEDLINE, PsycINFO, PsyINDEX, CINAHL, ERIC. Risk of bias assessment will be conducted according to the guidelines of the Cochrane collaboration (interventional studies) and with the ROBINS-E tool (non-interventional studies). The grading of methodical quality will be assessed with the Newcastle-Ottawa Scale. Results: In systematic qualitative synthesis evidence will be summarised. A summary of the methodology and results of each of the studies included will be provided in table form. Conclusions: The systematic review will answer the question if there is a relationship between a cancer diagnosis and the divorce rate. A higher divorce rate due to a general or specific cancer diagnosis would reveal a requirement for psychosocial support targeted not only on the cancer patients themselves but also on their spouses.
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 | 0.024 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".