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Research Article
Determinants of Psychological Distress Among Healthcare Workers in a Reference Medical Oncology Unit in Cameroon
Berthe Sabine Esson Mapoko*
,
Esther Dina Bell,
Marie Josiane Ntsama Essomba,
Veronique Batoum Mboua,
Etienne Atenguena,
Dominique Anaba,
Anne Sango,
Ruth Mapenya,
Anne Marthe Maison,
Sidonie Ananga,
Ambroise Ntama,
Zacharie Sando,
Olga Bassong Mankollo,
Julienne Ngo Likeng
Issue:
Volume 13, Issue 4, December 2025
Pages:
152-158
Received:
14 September 2025
Accepted:
9 October 2025
Published:
30 October 2025
Abstract: Introduction: Healthcare professionals working in oncology are exposed to intense and constant stressors, given the severity of the diseases and frequent confrontation with patient death, which can lead to significant psychological distress and professional burnout. This study's objective was to identify the sociodemographic, social, and work-related determinants contributing to this distress among the nursing staff in the medical oncology department of the Yaoundé General Hospital. Materials and Methods: This was a qualitative study conducted from July to December 2017 in a reference medical oncology unit in Cameroon. The study population comprised the entire nursing and medical staff of the department. A non-probability, exhaustive sampling method was used, resulting in seventeen healthcare workers (13 women, 4 men; 10 nurses, 7 doctors) participating. Data were collected through audio-recorded individual semi-structured interviews and subsequently analyzed using manual content analysis. Results: The analysis revealed that psychological distress is a multifaceted issue driven by three main categories of determinants. Sociodemographic factors identified as sources of pressure included female gender, place of residence (linked to long commutes and traffic stress), family pressure, and personal/financial difficulties. Social factors highlighted varying coping strategies, from prayer and communication to emotional detachment (disconnection/splitting) in the face of patient suffering and death. Work-related environmental determinants were found to be the primary cause of distress, unanimously described by staff. These organizational factors included an unbearably heavy workload due to understaffing, stress from managing patient pain and death (often reduced to administrative tasks), difficult interprofessional communication between nurses and doctors, a severe lack of continuous professional training, and a complete absence of gratification or recognition from management. Conclusion: Psychological distress among oncology healthcare professionals is strongly associated with sociodemographic, social, and, critically, pervasive work-related environmental determinants. The heavy and poorly managed workload, coupled with a lack of institutional support, training, and recognition, are major sources of suffering that require urgent attention from hospital administrators to mitigate psychosocial risks.
Abstract: Introduction: Healthcare professionals working in oncology are exposed to intense and constant stressors, given the severity of the diseases and frequent confrontation with patient death, which can lead to significant psychological distress and professional burnout. This study's objective was to identify the sociodemographic, social, and work-relat...
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Research Article
Study Shows No Awareness of Breast Cancer Screening Among Women in Rural Areas in Coastal Karnataka Compared to Nearby Urban Areas
Issue:
Volume 13, Issue 4, December 2025
Pages:
159-166
Received:
22 September 2025
Accepted:
11 October 2025
Published:
3 December 2025
Abstract: Background and objectives: Breast cancer (BC) is the leading cancer in Indian women. This study examined relationships between demographic variables of women in rural Vitla and urban areas of Mangalore, Moodbidri and Puttur in Dakshina Kannada District and their awareness of BC screening policy, BC and health insurance. The objectives were to know if rural women have adequate knowledge of BC screening. Methods: 100 women cashew factory workers in Vitla were given an oral questionnaire in Kannada and 65 women from urban areas of Mangalore, Moodbidri and Puttur were given the questionnaire in English. Results: Answer sets were analysed, results indicated no significant correlation between respondents’ education and awareness about BC Screening, Self-Examination (BSE) and government screening policies in Vitla (Dakshina Kannada). No relationship was observed between age and awareness. Women in Vitla (Mangalore is 40 km away from Vitla), were aware of BC and had Ayushman Bharat cover; yet they had no idea about screening or its policy but agreed to screen if motivated. All urban women had received higher education, some had heard of BC screening and had health insurance yet were unwilling to screen. Others had not heard of screening but were willing to go. Interpretation and conclusions: India has a 2016 Government policy to screen BC which has not reached all rural women. It must be implemented effectively as early diagnoses and detection can reduce mortality in BC.
Abstract: Background and objectives: Breast cancer (BC) is the leading cancer in Indian women. This study examined relationships between demographic variables of women in rural Vitla and urban areas of Mangalore, Moodbidri and Puttur in Dakshina Kannada District and their awareness of BC screening policy, BC and health insurance. The objectives were to know ...
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Research Article
Determinants of Low Uptake of Cervical Cancer Screening Among Sexually Active Women in an Urban Health District in Cameroon
Issue:
Volume 13, Issue 4, December 2025
Pages:
167-172
Received:
11 November 2025
Accepted:
26 November 2025
Published:
29 December 2025
DOI:
10.11648/j.crj.20251304.13
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Abstract: Background: Cervical cancer remains a major public health concern in low- and middle-income countries. Screening uptake in Cameroon is far below the World Health Organization’s elimination targets. This study assessed socio-demographic, economic, and informational determinants of low cervical cancer screening uptake among sexually active women in an urban district of Yaoundé. Methods: A cross-sectional study was conducted from September to October 2023 in the Biyem-Assi Health District. A convenience sample of 250 sexually active women aged 25–59 years completed a pre-tested structured questionnaire. Logistic regression was used to identify factors associated with non-participation. Significance was set at p < 0.05. Results: Overall, 89.0% of participants had never been screened. Independent predictors of non-participation were being single (AOR 5.79; 95% CI 3.60–9.45), lack of awareness of screening centers (AOR 5.02; 95% CI 1.24–20.29), no health insurance (AOR 3.91; 95% CI 1.70–8.98), poor knowledge of cervical cancer (AOR 3.16; 95% CI 1.12–8.94), unemployment (AOR 2.16; 95% CI 1.18-4.00), and having ≤1 child (AOR 1.94; 95% CI 1.21-3.12). Conclusion: Cervical cancer screening uptake is critically low in this urban population. The main barriers relate to socioeconomic vulnerability and lack of specific information on where screening services are offered. Improving service visibility, reducing costs, and integrating screening into routine health services may help increase uptake, although further research is required to evaluate feasibility and impact.
Abstract: Background: Cervical cancer remains a major public health concern in low- and middle-income countries. Screening uptake in Cameroon is far below the World Health Organization’s elimination targets. This study assessed socio-demographic, economic, and informational determinants of low cervical cancer screening uptake among sexually active women in a...
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Research Article
Accurate Prediction of Survival Based on Kaplan–Meier Analytics
Philip de Melo*,
Michele DiLella,
Tameka Holman,
Shakira McElveen
Issue:
Volume 13, Issue 4, December 2025
Pages:
173-185
Received:
27 November 2025
Accepted:
10 December 2025
Published:
29 December 2025
DOI:
10.11648/j.crj.20251304.14
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Abstract: This study integrates Kaplan–Meier survival analysis with the Stochastic and Augmented Interpretable Health Analytics (SAIHA) framework to model long-term survival in pancreatic cancer, a malignancy characterized by late diagnosis, rapid progression, and poor prognosis. The Kaplan–Meier estimator was first employed to nonparametrically characterize empirical survival probabilities across the observed follow-up period, capturing censoring patterns and short-term mortality dynamics without imposing distributional assumptions. This step provided a transparent baseline representation of survival up to approximately four years post-diagnosis, where empirical data density remains sufficient for reliable estimation. To address the limitations of traditional Kaplan–Meier analysis in extrapolating beyond observed follow-up, the SAIHA framework was then applied using a Weibull survival model to propagate uncertainty, incorporate population heterogeneity, and generate probabilistic survival projections into the long-term horizon. The Weibull distribution was selected for its flexibility in modeling monotonic hazard functions commonly observed in aggressive cancers and for its interpretability within clinical contexts. Parameter uncertainty was explicitly modeled to reflect variability in disease progression and treatment response across patients. The combined model predicts a pronounced decline in survival beyond year four, with the most likely five-year survival probability estimated near 3% and a median six-year survival approaching 1.5%. These projections align with known epidemiological patterns of pancreatic cancer and underscore the persistent lethality of the disease despite advances in therapy. Importantly, the SAIHA framework provides full survival distributions rather than point estimates, enabling clinicians and researchers to assess uncertainty bounds and tail risks associated with long-term outcomes. Overall, the integrated Kaplan–Meier–SAIHA approach extends classical survival analysis by combining empirical rigor with stochastic, distribution-aware forecasting. This methodology offers a robust and interpretable framework for high-risk clinical prediction, supporting more informed decision-making in oncology research, population health modeling, and precision medicine applications.
Abstract: This study integrates Kaplan–Meier survival analysis with the Stochastic and Augmented Interpretable Health Analytics (SAIHA) framework to model long-term survival in pancreatic cancer, a malignancy characterized by late diagnosis, rapid progression, and poor prognosis. The Kaplan–Meier estimator was first employed to nonparametrically characterize...
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