Reflecting on ‘meaningful research’: A qualitative secondary analysis
Bibliographic record
Abstract
Reflection on 'meaning' and 'meaningful research' led the researchers to further explore data obtained in an original study which aimed to develop a strategy to improve the contribution of nurses towards health research. The purpose of this further exploration, using a qualitative secondary analysis, was to explore and describe what important stakeholders in research, as well as nurses, see as meaningful research. It was expected that this analysis might contribute to refine the strategy and shed light on how research can be communicated to nurses as a more meaningful activity. The original data sets, namely 28 lists of open-ended questions and eight transcripts of focus group interviews, were analysed, using content analysis. The results show that there are similarities, but differing emphasis, between the viewpoints of the mentioned stakeholders and nurses. It is recommended that stakeholders in research, including nurses, need to establish and work in respectful, supportive, research capacity building partnerships when conducting research. Following this approach might lead to research being understood and experienced by nurses as a meaningful activity.
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 | 0.057 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".