Bridging individual and population perspectives: a qualitative inquiry into health activation with population health experts
Bibliographic record
Abstract
BACKGROUND: Population health activation is thought to extend beyond individual patient engagement, emphasising community-wide participation in health management. This contrasts with the original patient activation measure developed by Hibbard and colleagues in 2010. This qualitative study thus investigates how population health experts conceptualise health activation on a population level. METHODS: Adopting a critical realist epistemology, semi-structured interviews were conducted with experts from health policy, health care systems, and community health across various institutions in Singapore. Purposive sampling was used to recruit experts with over five years of relevant experience. Interviews, lasting approximately 60 min each, were audio-recorded, transcribed verbatim, and analysed inductively using reflexive thematic analysis, following Braun and Clarke's framework. RESULTS: A total of 18 population health experts were interviewed. No new ideas or concepts emerged after the 15th interview; an additional three interviews were conducted to ensure saturation. Based on the findings, three major themes were generated: (1) Health activation is dynamic and influenced by external life circumstances such as competing responsibilities; (2) Emotional readiness and psychological resilience are critical prerequisites for meaningful health activation, beyond mere knowledge or skills; and (3) Social support networks and health care system structures are important in enabling or inhibiting health activation. Based on these insights, we propose a preliminary conceptual framework for health activation interventions that integrates emotional, social, and systemic dimensions. CONCLUSION: In summary, the findings advance the understanding of population health activation by proposing a broader and more dynamic conceptualisation, and they highlight the need for flexible adaptable strategies in population health, where engagement must be maintained over time and across diverse subpopulations. By focusing on emotional resilience, psychological preparedness and external support structures, health care systems can better promote and sustain individual health activation. Future research should empirically test this and incorporate additional perspectives from the public and patients.
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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".