Subsequent full publication of qualitative studies presented at United Kingdom Royal College of Nursing Research Conference 2015 and 2016: A follow‐up study
Bibliographic record
Abstract
A considerable proportion of quantitative research remains unpublished once completed. Little research has documented non-dissemination and dissemination bias in qualitative research. This study aimed to generate evidence on the extent of non-dissemination in qualitative research. We followed a cohort of qualitative studies presented as conference abstracts to ascertain their subsequent publication status. We searched for subsequent full publication in MEDLINE, in the Cumulative Index to Nursing & Allied Health Literature and in Google Scholar. We matched abstracts to subsequent publications according to authors, method of data collection and phenomenon of interest. Fisher's exact test was calculated to examine associations between study characteristics and publication. Factors potentially associated with time to publication were evaluated with Cox regression analysis. For 91 of 270 included abstracts (33.70%; 95% CI 28.09%-39.68%), no full publication was identified. Factors that were found to be associated with subsequent full publication were oral presentation (OR 4.62; 95% CI 2.43-8.94) and university affiliation (OR 1.96; 95% CI 1.05-3.66). Compared to oral presentations, studies presented as posters took longer time to reach full publication (hazard ratio 0.35, 95% CI 0.21-0.58). This study shows that it was not possible to retrieve a full publication for over one-third of abstracts. Our findings suggest that where this non-dissemination is systematic, it may lead to distortions of the qualitative evidence-base for decision-making through dissemination bias. Our findings are congruent with those of other studies. Further research might investigate non-dissemination of qualitative studies in other disciplines to consolidate our findings.
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 | 0.198 | 0.533 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".