Alcohol’s harm to others: Using qualitative research to complement survey findings
Bibliographic record
Abstract
Manton, E., MacLean, S., Laslett, A., & Room, R. (2014). Alcohol’s harm to others: Using qualitative research to complement survey findings. The International Journal Of Alcohol And Drug Research, 3(2), 143-148. doi:http://dx.doi.org/10.7895/ijadr.v3i2.178Aim: The purpose of this study was to identify the potential contribution of qualitative research to future Alcohol’s Harm to Others (AHTO) survey research and some of the potential difficulties that may be encountered when conducting studies of this nature.Design: Qualitative, in-depth semi-structured telephone interviews.Setting: Australia.Participants: Potential participants were those who responded, in the telephone land-line-based Australia-wide AHTO survey in either 2008 or 2011, that a child or children for whom they had responsibility had been harmed “a lot” or “a little” by someone else’s drinking, and who also indicated that they were willing to be recontacted for future research interviews. Ten participants who selected the response “a lot” and 10 who selected “a little” were interviewed.Measures: Interviews were audio recorded and professionally transcribed. Transcribed interviews were thematically analysed.Findings: The qualitative study analysis enabled access to detailed stories, clarification of the validity and meanings of survey measures, identification of questions for future surveys, and contextualization of survey findings. The analysis also suggested that samples of people who agree to discuss harm from others’ drinking with a researcher are likely to be skewed in particular ways.Conclusions: The approach to AHTO research described here incorporates both the persuasive power of whole-population survey research and the nuanced understanding provided through interpretation of in-depth qualitative interviews. It enables the presentation of more comprehensive information about the nature and extent of AHTO.
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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 | 0.181 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".