Road Safety Challenges in Sub-Saharan Africa: The Case of Ghana
Bibliographic record
Abstract
The importance of road safety in the developmental agenda of a country cannot be overemphasized. It is essential for effective socioeconomic development. However, several countries in the developing world are confronted by several challenges relating to road safety, which are inadequately investigated. These challenges further aggravate the already heavily burdened health-care systems. The aim of this study is, therefore, to determine and analyse road safety issues in Ghana aimed at contributing to national policy development, stakeholder engagements, and public safety education campaigns on road traffic collision. A qualitative study by one-on-one interviews with individuals ( n = 97) in road safety leadership positions was performed from November 2018 to February 2019. The interviews were audio-recorded and transcribed. Data analysis was conducted using a constant comparative methodology approach facilitated by Atlas.ti 8.0 software. Important road safety challenges that were identified by the respondents were categorized into six thematic areas, namely, institutional, executional, managerial and operational, attitudinal and behavioural, research, and financial and investment challenges. We recommend that the government and stakeholders alike should tackle these challenges by building a collaborative environment where everyone is involved in the process of developing and implementing strategies aimed at overcoming these challenges as they arise. There is also the need to address the epidemic carnage of road traffic injuries, many of which are preventable since they arise from human actions and inactions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".