Impact of the COVID-19 Pandemic on Cancer Care in Iraq: Exploratory Research
Bibliographic record
Abstract
Background: The COVID-19 pandemic has a tangible impact on the health care systems globallyand is represented by interruption of the usual services in many health facilities and exposingvulnerable patients to significant risks.Objectives: We aimed to evaluate this pandemic’s impact on Iraq’s cancer care.Materials and methods: We conducted an exploratory study using a validated web-basedquestionnaire of 51 items. The questionnaire collected information on the capacity and servicesoffered at the Iraqi cancer centers, the magnitude of care disruption, reasons for the disruption,challenges faced, patient harm estimation, and the interventions implemented during the pandemic.Results: 18 cancer centers from 11 Iraqi governorates took part between 21st April and 8th May2020. These centers were serving around 18,867 new patients per year. Most of them (72.2%)were facing challenges in delivering their care during the pandemic. Although 44.4% of the centersreduced their services as part of a pre-emptive strategy, other reported reasons included lack ofpersonal protective equipment (22.2%), an overwhelmed system (11.1%), and a restricted approachto medications (11.1%). Missing at least one therapy cycle by > 10% of the patients was reportedin 38.9% of the centers. Participants have reported that their patients were exposed to potentialharm from interruption of cancer-specific care (44.4%) and non-cancer-related care (33.3%).Conclusion: The negative impact of the COVID-19 pandemic on cancer care in Iraq is evident.Additional research to estimate such an effect at the patients’ level and the required measures tocounteract this problem is vital.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".