Incorporating a gender lens into nutrition and health-related policies in Fiji: analysis of policies and stakeholder perspectives
Bibliographic record
Abstract
Abstract Background Gender equality, zero hunger and healthy lives and well-being for all, are three of the Sustainable Development Goals (SDGs) that underpin Fiji’s National Development Plan. Work towards each of these goals contributes to the reduction of non-communicable diseases (NCDs). There are gender differences in NCD burden in Fiji. It is, however, unclear whether a gender lens could be more effectively included in nutrition and health-related policies. Methods This study consisted of three components: (i) a policy content analysis of gender inclusion in nutrition and health-related policies (n = 11); (ii) policy analysis using the WHO Gender Analysis tool to identify opportunities for strengthening future policy; and (iii) informant interviews (n = 18), to understand perceptions of the prospects for gender considerations in future policies. Results Gender equality was a goal in seven policies (64%); however, most focused on women of reproductive age. One of the policies was ranked as gender responsive. Main themes from key informant interviews were: 1) a needs-based approach for the focus on specific population groups in policies; 2) gender-related roles and responsibilities around nutrition and health; 3) what is considered “equitable” when it comes to gender, nutrition, and health; 4) current considerations of gender in policies and ideas for further gender inclusion; and 5) barriers and enablers to the inclusion of gender considerations in policies. Informants acknowledged gender differences in the burden of nutrition-related NCDs, yet most did not identify a need for stronger inclusion of gender considerations within policies. Conclusions There is considerable scope for greater inclusion of gender in nutrition and health-related policies in Fiji. This could be done by: 1) framing gender considerations in ways that are actionable and inclusive of a range of gender identities; 2) undertaking advocacy through actor networks to highlight the need for gender-responsive nutrition and health-related policies for key stakeholder groups; 3) ensuring that data collected to monitor policy implementation is disaggregated by sex and genders; and 4) promoting equitable participation in nutrition related issues in communities and governance processes. Action on these four areas are likely critical enablers to more gender equitable NCD reduction in Fiji.
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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".