Family Conflicts and Technology Use: The Voices of Grandmothers
Bibliographic record
Abstract
Objective Our aim was to understand family conflicts, specifically those involving grandmothers, related to use of new communication technologies. Background Research shows that tension between family members in intergenerational contexts arises in relation to technology. This is especially common when attitudes toward technology differ among family members. Differing opinions around technology use create gaps in skills and perceived competence. Grandparents' voices about the challenges of perpetual connectivity in family settings are absent in the research on technology domestication and mediation. Method To fill this gap, semistructured group interviews were conducted with women in Canada, Colombia, Israel, Italy, Peru, Romania, and Spain. All women were aged 65 years and older, had grandchildren, and used information and communication technology (ICT). Results Grandmothers experienced conflicts when interacting with grandchildren due to ginability to recognize online threats. Asking for help in managing different applications could be a source of family conflicts. Embarrassment and unease is reduced when grandmothers call grandchildren for help, rather than receive assistance from their adult children. Conflictual moments also emerged around the use of ICT at family dinners or other gatherings, with grandmothers showing more tolerance in this context for grandchildren than for their adult children. Conclusion Family conflicts over technology use may differ when involving adult children versus grandchildren. Implications The voices of grandmothers express the importance of permanent and affordable opportunities for people to receive assistance in technology use outside of family contexts.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".