Clarifying researchers’ subjectivity in qualitative addiction research
Bibliographic record
Abstract
Aims: In addiction research, non-constructionist traditions often question the validity and reliability of qualitative efforts. This study presents techniques that are helpful for qualitative researchers in dissecting and clarifying their subjective interpretations.Methods: We discuss three courses of action for inspecting researchers’ interpretations when analyzing focus-group interviews: (i) adapted summative content analysis, (ii) quantification of researchers’ expectations; and (iii) speaker positions. While these are well-known methodological techniques in their own rights, we demonstrate how they can be used to complement one another.Results: Quantifications are easy and expeditious verification techniques, but they demand additional investigation of speaker positions. A combination of these techniques can strengthen validity and reliability without compromising the nature of constructionist and inductive inquiries.Conclusions: The three techniques offer valuable support for the communication of qualitative work in addiction research. They allow researchers to assess and understand their own initial impressions during data collection and raw analysis. In addition, they also serve in making researchers’ subjectivity more transparent. All of this can be achieved without abandoning subjectivity, but rather making sense of it.
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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 | 0.571 | 0.528 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.013 | 0.064 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".