Interviewee Transcript Review: assessing the impact on qualitative research
Bibliographic record
Abstract
BACKGROUND: This paper assesses interviewee transcript review (ITR) as a technique for improving the rigour of interview-based, qualitative research. ITR is a process whereby interviewees are provided with verbatim transcripts of their interviews for the purposes of verifying accuracy, correcting errors or inaccuracies and providing clarifications. ITR, in various forms, is widely used among qualitative researchers, however there is limited methodological guidance on how it should be employed and little is known about its actual impact on the transcript, the data, the interviewee or the researcher. METHODS: ITR was incorporated into a qualitative research study in which 51 key informant interviews were conducted with a range of senior stakeholders within the Canadian health care system. The changes made by interviewees to their transcripts were systematically tracked and categorized using a set of mutually exclusive categories. RESULTS: The study found that ITR added little to the accuracy of the transcript and may create complications if the goal of the researcher is to produce a transcript which reflects precisely what was said at the time of the interview. The advantages of ITR are that it allows interviewees the opportunity to edit or clarify information provided in the original interview, with many interviewees providing corrections, clarifications, and in some cases, adding new material to their transcripts. There are also potential disadvantages, such as a bias created by inconsistent data sources or the loss of data when an interviewee chooses to remove valuable material. The impact of ITR on the interviewee may be both positive and negative, depending on the specific circumstances and the nature of the study. The impact of ITR on the researcher was minimal in this study, but is again subject to specific circumstances of the research context. CONCLUSION: While ITR is employed by many researchers across numerous fields, the advantages of its use may be relatively small in terms of verifying the accuracy of qualitative interview transcripts. Researchers are advised to carefully consider both the potential advantages and disadvantages of ITR outlined in this paper before deciding to incorporate the practice within their qualitative study designs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.599 | 0.835 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".