Support services for people who have undergone treatment for head and neck cancer: an approach to evaluating services
Bibliographic record
Abstract
The purpose of this qualitative research was to (1) determine whether support services for people who have undergone treatment for head and neck cancer in Canterbury, New Zealand, align with current national and international guidelines and recommendations for best practice, (2) investigate the views of consumers and health service providers, about current support services and service needs, (3) develop recommendations for a coordinated support service. Interviews with consumers and with service providers, including multidisciplinary team members, were undertaken to collect information about support services for people with head and neck cancer in Canterbury. The analysis was undertaken in two stages. First, information was compared with current guidelines, and second, transcripts of interviews were analysed thematically, using a general inductive approach, to understand any issues arising for both consumers and providers. Comparison with guidelines identified many strengths and a few limitations of the current service compared with international best-practice. A number of other issues arose from the thematic analysis which suggested potential areas for improvement. Recommendations were made to support a best-practice, evidence-based coordinated support service for people with head and neck cancer in Canterbury. The method used to evaluate this service could be used in the evaluation of other intersectoral, multidisciplinary health services.
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 | 0.067 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".