Factors shaping the global political priority of addressing elder abuse: a qualitative policy analysis
Bibliographic record
Abstract
Background: Globally, 1 in 6 people aged 60 years and older experience elder abuse in the community annually, with potentially severe physical and mental health, financial, and social consequences. Yet, elder abuse remains a low global priority. We aimed to identify the factors accounting for the low global political priority of elder abuse. Methods: We systematically searched relevant peer-reviewed literature and organisational reports in multiple databases and interviewed 26 key informants in the field of elder abuse. We used policy frameworks developed by previous research into the determinants of the priority of global health issues, and a qualitative methodology to thematically analyse the literature and interviews through triangulation of the data. Findings: The main factors identified were related to the nature of the issue (the inherent complexity of elder abuse, pervasive ageism, insufficient awareness and doubts about prevalence estimates, and the intractability of the issue), the policy environment (the restricted ability in the field of elder abuse to capitalise on policy windows and processes), and the capabilities of the proponents of prevention of elder abuse (disagreements over the nature of the problem and solutions, challenges in individual and organisational leadership, and an absence of alliances with other issues). Interpretation: Around 25 years ago, elder abuse started to register on the global agenda. Since then, the global priority for prevention of elder abuse has barely increased. This study identifies several inter-related factors that account for the issue's low priority and opportunities for overcoming these challenges. Chief among these opportunities is the UN Decade of Healthy Ageing 2021-2030, a unique 10-year-long policy window to increase the political priority of the prevention of elder abuse. Funding: World Health Organization.
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 | 0.050 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".