Attitudes Toward School-Based Surveillance of Adolescents’ Social Media Activity: Convergent Parallel Mixed Methods Survey
Notice bibliographique
Résumé
BACKGROUND: US schools increasingly implement commercially available technology for social media monitoring (SMM) of students, purportedly to address youth mental health and school safety. However, little is known about how SMM is perceived by stakeholders, including the students who are the focus of these efforts. OBJECTIVE: We aimed to assess attitudes toward SMM in schools among 4 stakeholder groups and examine reasons for holding supportive, neutral, or unsupportive views toward the technology. We also sought to explore whether any differences in attitudes were associated with binary sex, race, ethnicity, sexual orientation, or gender identity. METHODS: In October 2019, we conducted a convergent parallel mixed methods web-based survey of young adults (aged 18-22 y; n=206), parents (n=205), teachers (n=77), and school administrators (n=41) via Qualtrics web-based panels. We included Likert-type survey items to assess perceived benefits, risks, and overall support of SMM in schools and test for differences based on stakeholder group or demographic characteristics. We also included open-ended questions, and the responses to these items were analyzed using thematic content analysis of reasons given for holding supportive, neutral, or unsupportive views. RESULTS: The tests of group differences showed that young adults perceived lower benefit (P<.001) as well as higher risk (P<.001) and expressed lower overall support (P<.001) of the use of SMM in schools than all other stakeholder groups. Individuals identifying as nonheterosexual also perceived lower benefit (P=.002) and higher risk (P=.02) and expressed lower overall support (P=.02) than their heterosexual counterparts; respondents who identified as people of racial and ethnic minorities also perceived higher risk (P=.04) than their White counterparts. Qualitative thematic content analysis revealed greater nuance in concerns about SMM. Specifically, the primary reasons given for not supporting SMM across all stakeholder groups were (1) skepticism about its utility, (2) perceived privacy violations, and (3) fears of inappropriate or discriminatory use of the data. Within the young adult group in particular, concerns were also raised about (4) unintended and adverse consequences, including the erosion of trust between students and school institutions and administrators, and the chronic adverse effects of constant or prolonged surveillance. Thematic analysis also showed that individuals in every stakeholder group who indicated overall support of SMM were likely to cite the potential for enhanced school safety as the reason. Young adults' overall stances toward SMM were the most polarized, either strongly for or strongly against SMM, and responses from teachers indicated similar polarization but more often favored support of SMM in schools. CONCLUSIONS: This study found differing perspectives among stakeholder groups regarding SMM in schools. More work is needed to assess the ways in which this type of surveillance is being implemented and the range and complexity of possible effects, particularly on students.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».