Technology-facilitated violence against child welfare workers: A qualitative analysis
Bibliographic record
Abstract
Background: Advances in technology have influenced the provision of child welfare services in a variety of ways creating both new opportunities to improve services and new risks, including the risk of technology-facilitated violence (TFV) against workers. Objective: This study aimed to: better understand the nature and impacts of TFV against child welfare workers; identify strategies employed by child welfare workers to minimize risk and manage the impacts of TFV; and explore how organizational responses may mitigate or exacerbate the impact of violence once it occurs. Participants: Eleven child welfare workers from across Canada who worked in a variety of child welfare roles including intake and investigation, permanency planning, guardianship, and crisis intervention participated in interviews. Methods: Using long-interview method of data collection, the researchers adopted a discovery-oriented qualitative design, employing the constructivist grounded theory method. Results: Participants reported a variety of electronic means used to communicate with clients including email, social media platforms, direct messaging, and text messaging. While electronic means of communication provided opportunities for engagement both personally and professionally, it also carried the risk that abusive and threatening comments could be transmitted to workers in a new way. Results revealed an escalating progression of TFV. First, participants were subject to repeated abuse, harassment, and threats conveyed through email, text messages, and work-related social media. Next, abusive clients used information from online sources to contact workers through their personal social media, blurring the boundaries between their personal and professional lives. In addition, images and information about workers were shared on public and communal social media pages, inciting others to join in the abuse and harassment. Finally, the increased visibility of workers resulted in direct, in-person confrontations in the community not only by clients but also by members of the public. Participants indicated that organizations were insufficiently prepared to deal with TFV and as a result, workers were largely left to deal with TFV on their own. Conclusion: As identified by participants in this study, there is an urgent need within child welfare for the development of policies and procedures related to TFV, training for workers on TFV prevention and mitigation strategies, and supports to mitigate the effects of TFV when it occurs.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".