A Systematic Review of Worldwide Cancer Nursing Research
Bibliographic record
Abstract
The aim of this study was to assess the cancer nursing research papers published in the past decade; identify their characteristics in terms of country of origin, participants, settings, diagnostic foci, and methodologic choices; and evaluate their quality. A systematic review was carried out of all published papers in the Cumulative Index of Nursing and Allied Health Literature between the years 1994 and 2003, using the keywords "cancer," "nursing," and "research." A total of 619 papers met inclusion criteria and were evaluated by 5 researchers. Almost half the papers were derived from the United States (49.1%), followed by the UK, Sweden, Canada, and Australia. In more than half of the published papers (52.2%), health professionals (mostly nurses) were the studies' participants. Also, much of the published research used patients with mixed diagnosis, or patients with breast or hematologic cancers. Two-thirds of the studies were quantitative, whereas most studies were descriptive in nature. The quality of both quantitative and qualitative studies was low, with only a small percentage meeting the highest quality criteria. Studies reporting funding and those published in journals with an impact factor showed a higher quality score than those not reporting funding or not published in journals with an impact factor. Cancer nursing research is still in a developmental stage, although it has made a considerable contribution to the evidence base of the discipline. A number of issues need to be tackled before we improve our output, such as organizational or workforce issues, infrastructure support, funding, and methodologic challenges.
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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 | 0.040 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.047 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".