Bibliographic record
Abstract
Internet scams have become more sophisticated and prevalent in countries such as Canada, the US, the UK, and Australia. Australia has made some progress in effective scam intervention strategies and seen possible growth in public awareness. However, there is a lack of insight into factors associated with profound shame and embarrassment, emotional distress such as anxiety and depression, and trauma and suicide in scam victims. To fill this gap, this perspective paper aimed to provide insight into the factors associated with the negative mental health impacts of internet scams by integrating a narrative literature review with a victim case study detailing a group's experience of an investment scam in Australia. It found that internet scams cause emotional and social issues like depression, anxiety, trauma, and isolation, mostly prolonged upon substantial loss. The author's insight into the intensely negative mental health impacts of an investment scam allows for the presentation of a group who struggled to access adequate support and mental health care in their response to insidious organized crime. Better education, resilience-building, and support systems are needed. These shortcomings call for strategies for tailored digital mental health services such as emotionally attuned, trauma-informed digital companionship through human-like artificial intelligence (AI) applications.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".