A needs assessment to determine the need for respiratory therapy in complex continuing care: A methods paper.
Bibliographic record
Abstract
BACKGROUND: There is an emerging demand for complex continuing care for patients who are too ill to safely return home, but for whom hospitalization in an acute care environment is unnecessary or inappropriate. Despite the need and medical complexity of these patients, few respiratory therapists are practising in this environment, and little evidence exists to guide the implementation of respiratory therapy services in this setting. OBJECTIVE: In response to a perceived need for greater respiratory services at Saint Vincent Hospital (Ottawa, Ontario), a needs assessment was undertaken to assess the prevalence of respiratory diseases and for increased respiratory therapist coverage at this complex continuing care hospital. METHODS: An initial literature review was conducted to guide the assessment, and identified only one tool of relevance, which was obtained and formed the basis of the further development of tools for collecting data at the hospital level and on patient care units at the facility. This needs assessment tool was expanded to include priority areas of relevance that fall within the scope of practice of respiratory therapists, and was supplemented by the analysis of administrative databases and qualitative data gathered through unit walkthroughs and unstructured key informant interviews. A health systems framework was used to structure recommendations for the development of interventions and programs for this patient population. RESULTS: The burden of respiratory disease was significant, and included a high prevalence of inhaled medication and oxygen use, and a significant workload that could be attributed to addressing the respiratory needs of patients. CONCLUSION: A range of tools and methods are needed to conduct needs assessments for respiratory therapy in complex continuing care. Using multiple data sources, a significant burden of respiratory diseases was present at the Saint Vincent Hospital; further studies in other complex continuing care hospitals are needed to understand the significance of these findings among this patient population more generally.
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 | 0.043 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".