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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".