Strategizing a Game Plan
Bibliographic record
Abstract
BACKGROUND: When cancer patients at the end of life stay at home, family caregivers are often directly implicated in the care. One challenge is pain management. They are often unprepared and unsupported as they attempt to meet this responsibility. There are few studies that examine what this responsibility entails. OBJECTIVE: The purpose of this article was to present one important process extracted from the results of a grounded theory study of family caregiver management of the pain of palliative cancer patients at home. Specifically, it will look at how family caregivers strategize to plan for pain management. METHODS: Twenty-four family caregivers participated. They were recruited using purposeful and then theoretical sampling. The data sources were taped, transcribed, semistructured interviews and field notes. Data analysis used Strauss and Corbin's open, axial, and selective coding. RESULTS: The process "strategizing a game plan," which includes the subprocesses of "accepting responsibility," "seeking information," and "establishing a pain management relationship" will be presented in this article. These processes were integral to pain management at home. CONCLUSIONS: These results highlight that, even when family caregivers accept responsibility for pain management, they are not always well prepared and require appropriate support to ensure optimal pain control. IMPLICATIONS FOR PRACTICE: Understanding that family caregivers are continuously engaging in specific processes as they prepare for and implement pain management strategies can help health care providers tailor their interventions to specific parts of the complex process of family caregiver management of palliative care cancer patients' pain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".