Bibliographic record
Abstract
The healthcare industry is the largest and fastest growing industry in the world; however, being a part of the healthcare system involves being at an increased risk of experiencing mental health problems, loneliness, stress, and increased susceptibility to experiencing compassion fatigue related to the emotional wear associated with providing patient-centered care. Healthcare workers include, but are not limited, to physicians, nurses, allied health professionals, and psychologists. Often, healthcare workers place the needs and wellbeing of patients before their own. This article aims to highlight the occupational hazards of working in the healthcare field, the physical and emotional isolation associated with clinical practice, managing distressing behaviors by patients, and reviewing the systemic barriers influencing the development and management of moral distress. We further aim to bring attention to the need for healthcare professionals to place self-care at the forefront of their therapeutic repertoire through various individualized strategies, through the importance of building moral resilience, and the shift towards improving workplace spirituality. Practicing self-care can address the consequences of neglecting one’s own wellbeing, positively impact the ability to provide better quality patient care, and benefits relationships with patients, loved ones, and of most importantly with oneself.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".