A survey of the implementation of participation strategies to enhance inclusive workplaces in the public and private educational systems in Lagos State
Bibliographic record
Abstract
This survey examined the implementation of participation strategies to enhance inclusiveness of workplaces in the public and private educational systems in Lagos State, Nigeria for mental health service users. The study employed a multistage sampling technique to select 346 school administrators. The survey was largely quantitative, but three open-ended questions subjected to qualitative analysis were included to add depth to the analysis. Data were analyzed using descriptive, bivariate inferential and multivariate statistical methods. The results described only a minimal adoption of a collaborative strategy in schools. Private and public schools minimally engage in networking and cooperative structures. It was revealed that anti-stigma programs for prevention and intervention were in place in both private and public schools. A discrepancy in the government level of support was observed in private and public schools as governments tend to focus more on public schools. The findings established that information, psychoeducation, advocacy and referral/treatment significantly predicted inclusive outcomes in the two settings. However, school-type was significant in explaining variations in treatment/referral. Public schools have a higher treatment rate than private schools in the three study areas. More so, the hypothesis test results established a statistically significant mean difference between the networking and cooperative structures adopted by private and public schools with public schools dominating. Only the presence of cooperative structures accounted for the difference. Overall, it was found that the implementation of the inclusive framework (collaborative strategy) does not meet the objectives stipulated by the Federal Ministry of Health (FMoH, 2019) Human Resources Health (HRH) guidelines because the necessary structures and programs are lacking. There seems to be a power imbalance between mental health agencies and schools in program execution, with schools being a more passive consumer of mental health services as revealed in the qualitative data. It was recommended that both government and citizens have a role to play in fostering inclusion. The study highlighted the need for schools’ active involvement in programs planning for the effectiveness and continuity of this initiative.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".