Aktivitetsproblemer hos borgere som udelukkende er henvist til fysisk ambulant genoptræning
Bibliographic record
Abstract
Baggrund: Der er sammenhæng mellem ældre menneskers deltagelse i forskelligartede aktiviteter og sundhed, dog indeholder henvisninger til kommunal genoptræning hovedsageligt beskrivelser af fysiske vanskeligheder. Formål: At kortlægge hvorvidt den ældre borger, henvist til fysisk ambulant genoptræning, oplever aktivitetsproblemer og i så fald hvilke, samt hvordan de prioriteres. Metode: Studiet var kvalitativt i sin ansats og forankret i forfatterens kliniske praksis. Informanterne var > 60 år (n=11) og henvist til genoptræning i den kommune, hvor de boede. Gennem to uger blev der foretaget tværfaglige vurderinger, informanterne blev interviewet af en ergoterapeut ved anvendelse af instrumentet Canadian Occupational Performance Measure (COPM), et reliabelt og responsivt instrument. De indsamlede data blev bearbejdet gennem inspiration af en kvalitativ indholdsanalyse; først en manifest og derefter en latent analyse. Resultat: Studiet fandt, at borgerne oplevede aktivitetsproblemer relateret til stort set alle temaer indenfor COPM egenomsorg, arbejde og fritid. Konklusion: Studiet indikerer behov for fast procedure for ergoterapeutisk vurdering, hvor et standardiseret instrument til fremme af klientcentreret praksis anvendes COPM. Grundet populationens størrelse kan der ikke konkludere på sammenhæng mellem borgeres oplevelse af aktivitetsproblemer og henvisningsbeskrivelserne. Der er behov for yderligere studier.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".