How Can Social Media Lead to Co-Production (Co-Delivery) of New Services for the Elderly Population? A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The future of health care services in the European Union faces the triple challenges of aging, fiscal restriction, and inclusion. Co-production offers ways to manage informal care resources to help them cater for the growing needs of elderly people. Social media (SM) is seen as a critical enabler for co-production. OBJECTIVE: The objective of this study was to investigate how SM-private Facebook groups, forums, Twitter, and blogging-acts as an enabler of co-production in health and care by facilitating its four underlying principles: equality, diversity, accessibility, and reciprocity. METHODS: We used normalization process theory as our theoretical framework to design this study. We conducted a qualitative study and collected data through 20 semistructured interviews and observation of the activities of 10 online groups and individuals. We then used thematic analysis and drew on principles of co-production (equality, diversity, accessibility, and reciprocity) as a deductive coding framework to analyze our findings. RESULTS: Our findings point to distinct patterns of feature use by different people involved in care of elderly people. This diversity makes possible the principles of co-production by offering equality among users, enabling diversity of use, making experiences accessible, and encouraging reciprocity in the sharing of knowledge and mutual support. We also identified that explication of common resources may lead to new forms of competition and conflicts. These conflicts require better management to enhance the coordination of the common pool of resources. CONCLUSIONS: SM uses afford new forms of organizing and collective engagement between patients, carers, and professionals, which leads to change in health and care communication and coordination.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".