Technology-Facilitated Abuse in Intimate Relationships: A Scoping Review
Bibliographic record
Abstract
Technology-facilitated abuse (TFA) is a significant, harmful phenomenon and emerging trend in intimate partner violence. TFA encompasses a range of behaviours and is facilitated in online spaces (on social media and networking platforms) and through the misuse of everyday technology (e.g. mobile phone misuse, surveillance apps, spyware, surveillance via video cameras and so on). The body of work on TFA in intimate relationships is emerging, and so this scoping review set out to establish what types of abuse, impacts and forms of resistance are reported in current studies. The scoping review examined studies between 2000 and 2020 that focused on TFA within intimate partnerships (adults aged 18+) within the setting of any of these countries: the UK and Ireland, USA, Canada, New Zealand and Australia. The databases MEDLINE, CINAHL and Scopus were searched in December 2020. A total of 22 studies were included in the review. The main findings were that TFA is diverse in its presentation and tactics, but can be typed according to the eight domains of the Duluth Power & Control Wheel. Impacts are not routinely reported across studies but broadly fall into the categories of social, mental health and financial impacts and omnipresence. Similarly, modes of resistance are infrequently reported in studies. In the few studies that described victim/survivor resistance, this was in the context of direct action, access to legal or professional support or in the identification of barriers to resistance.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".