ASSESSING FAMILY CARE CONFERENCES IN LONG-TERM CARE: LESSONS LEARNED FROM CONTENT ANALYSIS
Bibliographic record
Abstract
End-of-life (EOL) communication in long-term care (LTC) is often inadequate and delayed, leaving residents dying with unknown preferences or goals of care. Poor communication with staff contributes to families feeling unprepared, distressed and unsatisfied negatively effecting bereavement. Family Care Conferences (FCC) aim to increase structured, systematic communication around goals and plans for EOL. FCCs were implemented as part of the ‘Strengthening a Palliative Approach to Care’ (SPA-LTC) project in four LTC sites in Ontario, Canada. The purpose of this sub-study is to evaluate: a) content, b) processes, and c) interdisciplinary engagement using mixed methods. Twenty-four FCCs were held for residents with a Palliative Performance Scale of 40% (nearing death) considered appropriate by staff. Data was collected from FCC forms (i.e., Family Questionnaires, Conference Summaries) and electronic charts. Through directed-analysis, data was analyzed using the Canadian Hospice Palliative Care Association’s ‘Square of Care’ model which includes eight domains of care: Disease Management, Physical, Psychological, Social, Practical, Spiritual, EOL, and Loss/Bereavement. Findings showed on average each FCC documented 66% of domains with physical and EOL care domains being used the most, and content about loss/bereavement documented the least. Use of FCC hard copy forms had benefits over standard electronic charts including: higher proportion of goals, timely completion, category for end-of-life care and accessibility. FCCs were attended by an average of three disciplines prompting holistic content although Personal Support Workers (PSW) and physicians attended minimally. Implications to optimize FCCs include tailoring use of FCCs forms, prompting bereavement discussion, furthering engagement of PSWs and physicians.
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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 | 0.062 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".