Participant acceptability of ‘digital footprint’ data collection strategies:evidence from the index participants of the ALSPAC birth cohort study
Bibliographic record
Abstract
Executive Summary • ALSPAC have identified that digital footprint records offer new research utility and are developing a strategy for incorporating these data into the study databank; • It is imperative that this novel use of participant data is understood and acceptable in order to successfully establish linkage to these data whilst maintaining trust in the wider study; • ALSPAC are testing participant views and expectations through qualitative research with participant groups to inform the design of our digital footprint strategy; • The evidence collected to date suggests that participants are initially unfamiliar with the rationale for using these forms of data in the ALSPAC research programme, and are unsure as to what benefits this can bring; • While some of these data are already in the public domain (e.g. twitter posts, air pollution measures), there is a perception that this is a substantial change in the studies data collection strategy; • Some participants consider that some forms of digital footprint data are more sensitive than others: with information on detailed online and transactional behaviours considered particularly sensitive, as are precise location information, bank records and medical records; • Participants consider that maintaining confidentiality, receiving clear information about any proposed data use and having mechanisms to retain control over the use of their data are important safeguards when considering requests to use these data; • Once the value and benefits are made clear, and subject to safeguard controls being in place, then many – but not all – participants suggest they would be accepting of this use of their records; • The use of digital footprint records will introduce ethical challenges, but the majority of these challenges align with existing ethical issues and can be accommodated within existing study frameworks
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.333 | 0.543 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".