Coping Strategies Used by Health Care Workers in Ecuador During the COVID-19 Pandemic: Observational Study to Enhance Resilience and Develop Training Tools
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has generated immense health care pressure, forcing critical decisions to be made in a socially alarmed environment. Adverse conditions have led to acute stress reactions, affective pathologies, and psychosomatic reactions among health personnel, which have been exacerbated by the successive waves of the pandemic. The recovery of the entire health system and its professionals has been hindered, making it essential to increase their resilience. OBJECTIVE: This study aimed to achieve 2 primary objectives. First, it sought to identify coping strategies, both individual and organizational, used by health care workers in Ecuador to navigate the acute stress during the early waves of the pandemic. Second, it aimed to develop training materials to enhance team leaders' capabilities in effectively managing high-stress situations. METHODS: The study used qualitative research techniques to collect information on institutional and personal coping strategies, as well as consensus-building techniques to develop a multimedia psychological tool that reinforces the resilience of professionals and teams in facing future crises. RESULTS: The findings from the actions taken by health care workers in Ecuador were categorized into 4 types of coping strategies based on Lazarus' theories on coping strategies. As a result of this study, a new audiovisual tool was created, comprising a series of podcasts, designed to disseminate these strategies globally within the Spanish-speaking world. The tool features testimonials from health care professionals in Ecuador, narrating their experiences under the pressures of providing care during the pandemic, with a particular emphasis on the coping strategies used. CONCLUSIONS: Ensuring the preparedness of health professionals for potential future outbreaks is imperative to maintain quality and patient safety. Interventions such as this one offer valuable insights and generate new tools for health professionals, serving as a case study approach to train leaders and improve the resilience capacity and skills of their teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".