“I Had a Needle, But This Program Gave Me the Thread”: A Caregiver Screening and Response Protocol
Bibliographic record
Abstract
CONTEXT: Family caregivers of individuals with advanced cancer face significant psychological and practical burdens, yet support for them remains limited. OBJECTIVES: This study aimed to develop and pilot a nurse-led screening and response protocol to address unmet support needs of family caregivers in an outpatient palliative care clinic (OPCC). METHODS: English-speaking adult family caregivers of patients attending an (OPCC) in a tertiary cancer center in Canada were recruited. Participants completed repeated needs screenings and engaged in follow-up nurse consultations, offering a tailored response. A satisfaction with care scale was administered at baseline and six months, and qualitative interviews explored participants' experiences. RESULTS: Of 31 family caregivers enrolled, all completed needs screening and a follow-up consultation at least once, and 61% completed both baseline and six-month measures. A pre-post analysis demonstrated a statistically significant improvement in satisfaction with psychosocial care. In qualitative interviews with a subset of 11 participants, family caregivers highlighted the value of receiving support independently from the patient, the acknowledgment of their needs as caregivers, having guidance to resolve concerns, and the facilitation of end-of-life planning. CONCLUSION: Routine screening and response protocols for family caregivers may help facilitate timely and tailored support. Early implementation may enhance recruitment and engagement.
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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 | 0.077 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".