Developing Grit, Motivation, and Resilience: To Give Up on Giving In
Bibliographic record
Abstract
Developing grit, motivation, and resilience within the pharmacy workforce has become a topic of increasing interest, heightened by the recent COVID-19 pandemic. Even prior to the global pandemic, the health care field has been associated with a rapidly changing, challenging, and pressured work environment that can often lead to stress and burnout. Developing resilience in health care workers has been identified as a strategy to combat burnout by improving their ability to thrive in stressful situations, thus enhancing physical and mental well-being. In this commentary, we consider the use of a resilience framework that encompasses the overlapping attributes of emotional balance and physical and mental strength to develop resilience. The importance of finding purpose and meaning is also explored within the framework, as well as the association between grit, motivation, autonomy, mastery, and connection. Practical strategies and reflections are outlined to challenge, inspire, and motivate the development of grit and resilience, in order to combat the challenges faced by pharmacists in a constantly changing health care system.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.016 |
| 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".