Utilization of trichological services by elderly persons — case study of Hairmitage Clinic
Bibliographic record
Abstract
The aim of this article is to explore the phenomenon of using the trichology services by the elderly 60+ customers. There are also some grounded assumptions that it grows under the impact of various factors: demographic factors, changing lifestyles and economic status and epidemiology of hair and head scalp diseases. In the theoretical part, the authors present the key theoretical frameworks and models that have so far been dedicated to explanation of various factors which could influence demand for various services among the elderly customers. These factors have been addressed in the Keynesian economic model, as well as in other varied socioeconomic approaches, such as “perpetual youth model” (Blanchard), “silver economy” concept, or public health approach represented in the concept of healthy active life expectancy (HALE) or the current progress in the way for the theory of ageing. Research method that has been used in this article is a case study, as the empirical part will cover the topic of utilization of (demand for) trichological services as recorded in the reservation system of Hairmitage Clinic, which specializes in various hair therapies. Time frame for the case study analysis will encompass 2021–2025 period (only 1st quarter at three Hairmitage locations [Warsaw, Gdynia and Katowice]) along with revenue of the whole company. This will be exploited for studying structure, dynamics of demand and for the comparative purposes (comparison of demand for trichological services between branches/offices, comparison of demand dynamics and revenue dynamics). As a result, the case study shows that demand in this age group grows faster than revenues of the whole company, showing elderly customers as a promise for future. This article is a call for further research on the market of trichology services and hair loss treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".