Community members speak –“Why are healthcare personnel subjected to disrespect and violence?”
Bibliographic record
Abstract
Purpose Healthcare personnel (HCP) are at high risk of facing violence globally. Their sanctity and respect are threatened by violence in healthcare settings. Mostly, this occurs at the hands of patients and community members. This study explores the reasons for disrespect and violence against HCP by patients and community members in selected communities of two provinces of Pakistan. Design/methodology/approach A qualitative study design was applied to develop an understanding of the processes that explained the community member's perception of disrespect and violence. A total of 12 focus group discussions (FGDs) with 11 community members on an average in each focus group and eight individual in-depth interviews (IDIs), each lasting for 40–50 min were conducted with community members. Data were analyzed thematically and guided by phenomenology. Findings The study found that community members perceived HCP as “angels on duty.” However they justified the anger of offenders as a result of shortcomings on the part of HCP and the healthcare settings. Furthermore, they blamed the chaos and ongoing crisis due to illiteracy and corruption within the society with existent poverty as triggers of violence and disrespect. Community members emphasized the role of media and labeled it as the game changer in building the image of HCP. They further stressed upon building competencies of the HCP and bridging the gap between HCP and communities to enhance respect and decrease violence on HCP. Practical implications Disrespect and violence against HCP can be minimized through improving competencies of HCP. Furthermore, media should play a positive role in safeguarding the rights of HCP and building their image. A holistic approach is suggested whereby all stakeholders should be actively involved in promoting awareness and respect for HCP. Originality/value Community members' perceptions have been taken into account, which is a unique and novel approach towards building inclusive communities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".