Global Mental Health: A Systematic Review of Burnout Syndrome in Latin American and Caribbean Teachers
Bibliographic record
Abstract
Background/Objectives: Despite significant ongoing investments in Indigenous health and wellbeing programs, evidence regarding program effectiveness is limited. Where these evaluations occur, the quality of this evidence may be impacted by process stages. Yet little is known about the effect of commissioning practices in the Indigenous space. This scoping review aims to codify the spectrum of commissioning practices used in Australia and internationally in the evaluation of Indigenous health and wellbeing programs. Method: Arksey and O’Malley and Levac et al. guided the review of literature from 2008 -2020 that address the commissioning of Indigenous health or wellbeing program evaluations in Australia, New Zealand, Canada and the United States. Forty-three documents were retrieved from four academic databases and the world wide web and coded against 13 Indigenous research and evaluation better practice principles derived from the literature. Results: The research shows five models used for commissioning evaluations of Indigenous health and wellbeing programs: a) top-down; b) participatory; c) codesign: delegative and e) Indigenous-led. Models range in the level of engagement with, and decision-making power awarded to, Indigenous communities. Levels which have significant influence on the way the findings are perceived by Indigenous peoples. For instance, models negating Indigenous power produce evaluations lacking in cultural safety and reciprocity. Conclusion: This scoping review, a first of its kind, provides insight into the spectrum of evaluation commissioning practices and how they align with better practice principles. Whilst, research suggests these better practice principles are often not considered, or their adherence hindered by a lack of institutional support, examples exist of commissioning practice supporting Indigenous engagement and leadership, which hold promise for broader application.
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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) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".