Maximizing older people’s access to primary health care centers in Lebanon: a co-design approach
Bibliographic record
Abstract
Limited access of older people to primary health care is a pressing issue in resource-constrained countries, particularly in Lebanon, amid the ongoing crisis. The co-design research approach is instrumental in addressing this problem, as it draws on people's experiences to generate practical and sustainable solutions. Using the design thinking framework, this co-design study involved 13 older people, family members, and primary healthcare service providers in co-designing solutions to maximize older people's access to primary healthcare centers in Lebanon. The design thinking process was implemented through seven in-person workshops, complemented by three individual interviews with older people involved as advisors. Co-designers identified the lack of preventive strategies for mental health and cognitive abilities as a key access barrier and a co-design challenge. The process resulted in two solution prototypes: (i) a plan to implement screenings for depression and cognitive problems, as a new service to be delivered at the primary health care center, and (ii) a creative brief for a social media campaign to raise awareness about the importance of preventive strategies to promote mental health and abilities among older people. This study suggests that enhancing preventive care to promote mental health and cognitive abilities can improve access by fostering the approachability, acceptability, appropriateness, and availability of services, as well as individuals' ability to perceive the need for care, seek, pay, and engage with care. Further research is needed to support the implementation of suggested solutions, to replicate this work in different settings across regions, and to address other identified challenges.
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 | 0.053 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".