Bibliographic record
Abstract
AIMS: This paper describes online recruitment and the email interviewing data collection method with women diagnosed with a viral sexually transmitted infection. The paper highlights the advantages of the method to researchers and participants when conducting research where face-to-face participation may difficult. BACKGROUND: Online recruitment and in-depth email interviewing have been used by only a small number of nurses internationally. The method enables inclusion of people who might otherwise be excluded from research, for reasons such as geographical distance, incompatible time frames, clinicians''gate-keeping' and participants' desire for anonymity for physical or emotional reasons. METHODS: In-depth email interviews were conducted with 26 women in New Zealand, United States of America, Canada and England who had a diagnosis of either human papilloma virus or genital herpes simplex virus. Data were collected during 2007-2008 and analysed using a poststructuralist, feminist thematic analysis. RESULTS: Participant retention was high. Women emphasized satisfaction with the process. Asynchronous interviews allowed for additional reflexivity in the researcher's responses and rich data generation. CONCLUSION: This method has the potential to enable nurses to include vulnerable and relatively inaccessible participants in 'sensitive' research. In-depth email interviews may generate rich data through a process participants deem to be of personal value.
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 | 0.059 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".