Meaning-Making and Managing Difficult Feelings: Providing Front-Line End-of-Life Care
Bibliographic record
Abstract
Managing grief and difficult emotions related to end-of-life (EOL) care is an often under-recognized part of the work of resident care aides (RCAs). In this interpretive analysis we explore the shared and socially constructed ideas that 11 RCAs in 1 Canadian city employ to make sense of death and the provision of EOL care. RCAs spoke of personal challenges involved in witnessing death and experiencing loss, as well as helplessness and frustration when they could not provide quality EOL care. RCAs invoked "consoling refrains" to manage grief, including "such is life," "they are better off," and "they had a full life." To manage guilt and moral distress, RCAs reminded themselves "I did my best" and "I experience rewards." Though these ideas help RCAs, some may need to be reframed through coaching and mentorship, to prevent unintended negative effects on care or the reproduction of ageist beliefs more broadly.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".