Online Personal Learning Networks for Older Adults: Impacts on Social and Mental Well-Being
Bibliographic record
Abstract
This study investigated retired older adults (age 55+) who use the Internet to facilitate their informal, self-directed learning by creating and maintaining their online personal learning networks (oPLNs), and how such use impacts their personal, social, and mental well-being. Guiding this examination were particular research questions that specifically queried the perceived value of their oPLNs to activate and self-direct their informal learning. The web-conferencing tool WebEx was used to conduct four synchronous online focus groups allowing a total of 15 voluntary, geographically dispersed participants from across Canada to share their experiences and insights. A thematic analysis of the focus group transcripts revealed themes informing how oPLNs facilitated their informal learning goals and influenced participants’ personal valuing of their online activity. As a component of results from the larger research study (Morrison, Litchenwald, & Krystkowiak, 2020; Morrison, Litchewald, & Tang, 2020; Morrison & McCutcheon, 2019) , the findings presented, drawn from the online focus group qualitative data, indicated positive perceived valuing of their informal learning via their oPLNs as well as indications of favorable social and mental well-being outcomes. Interpretive speculation is provided regarding how these informal online learning experiences and oPLNs in particular, may point to a favorable impact on similar retired older adults’ personal, social, and mental well-being.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".