Technologies for fostering intergenerational connectivity and relationships: Scoping review and emergent concepts
Bibliographic record
Abstract
The growing capabilities of technology are enabling increased support for communication and meaningful interactions that span distance, cultures, and generations. Interactions between youth (people younger than 19 years old) and older adults (people over 50 years old) have been shown to provide many benefits for both populations. Technology has a significant potential role to play in supporting intergenerational connectivity, however, little research has been done to specifically explore what role technology might have. This scoping review examines the literature to establish what technologies have been created or used to foster intergenerational interactions to infer overarching themes and propose directions for future research. A structured search was conducted through MEDLINE (PubMed), IEEE, CINAHL (EBSCOhost), ACM, and Scopus databases. Identified articles were screened first by title, then by abstract, and finally by full paper screening. Inclusion criteria were: written in English, contained both youth below 21 and adults over 50 years of age, peer-reviewed primary research (e.g. journal and conference publications) or published theses/dissertations, and presented design or use of a technology with the specific intention of fostering intergenerational connectivity. An inductive analysis was performed to identify emergent concepts related to the reviewed literature. A total of 36, 707 articles were identified; 77 were included in the review after the screening process. Five emergent concepts were identified: 1) Technology for intergenerational connectivity is an emerging field, 2) Interventions are primarily games-focused, 3) Research has been discipline-centric, 4) Lack of consistent vocabulary, and 5) Lack of consistent methodologies. This review is the first of its kind to scope the body of research related to technology fostering intergenerational connectivity. The breadth of methods reflects the new and multidisciplinary nature of this field and underscores the importance of creating shared approaches and vocabulary to increase knowledge sharing. This, in turn, could support more rapid and targeted progress in this field.
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".